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Special Issue "First Experiences with Chinese Gaofen-3 SAR Sensor"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 31 December 2018

Special Issue Editors

Guest Editor
Prof. Qingjun Zhang

China Academy of Space Technology, Beijing Institute of Space System Engineering, Beijing 100086, China
E-Mail
Phone: +86(0) 10 68747112
Interests: satellite system design; microwave remote sensing technology
Guest Editor
Prof. Zhenhong Li

School of Engineering, Newcastle University, Newcastle upon Tyne, UK
Website | E-Mail
Phone: +44 (0) 191 208 5704
Interests: synthetic aperture radar (SAR); interferometric SAR (InSAR); time series; precision agriculture; GNSS;
Guest Editor
Prof. Yunkai Deng

Institute of Electrics, Chinese Academy of Sciences, Beijing 100190, China
E-Mail
Phone: +86(0) 10 58887012
Interests: microwave remote sensing theory; radar system and signal processing
Guest Editor
Prof. Guisheng Liao

Xidian University, Shaanxi 710071, China
E-Mail
Phone: +86(0) 29 88201030
Interests: array signal processing; signal detection and estimation

Special Issue Information

Dear Colleagues,

The Chinese Gaofen-3 (GF-3) satellite was launched on 10 August, 2016, by the China Academy of Space Technology (CAST), and has been in operation since January, 2017. With its C‑band Synthetic Aperture Radar (SAR) sensor, featuring a large radar antenna that is 15 m in length, GF-3 is able to image the Earth's surface in all weather conditions, regardless of whether it is day or night. Circling the Earth in a sun-synchronous dusk-dawn orbit at 755 km in altitude, GF-3 can operate in 12 different working modes, from high-resolution (1 m) to extremely-wide-swath (650 km), from single to full polarization. Due to its wide incidence angles and both-sidelooking capability, GF-3 has a quick site access time of 3.5 days at most (1.5 day at 90% probability) to any point of the Earth.

Submissions are encouraged to cover a broad range of topics, which may include, but are not limited to, the following:

  • Mission status and planned/operational products
  • Satellite System Design/Manufacture
  • Calibration and validation activities of Gaofen-3 and instrument characteristics
  • Status of collaborative ground segments (CGS)
  • SAR polarimetry
  • SAR interferometry
  • Marine and maritime applications
  • Land cover/Land use
  • Geohazards and disaster monitoring
  • Critical infrastructure surveillance
  • Target detection
  • Tools, toolboxes and algorithms for analyzing Gaofen-3 data

Prof. Qingjun Zhang
Prof. Zhenhong Li
Prof. Yunkai Deng
Prof. Guisheng Liao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Gaofen-3
  • Calibration and Validation
  • Satellite System Design
  • SAR
  • Quantitative remote sensing
  • Multi-polarisation
  • SAR applications

Published Papers (46 papers)

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Research

Open AccessArticle An Improved BAQ Encoding and Decoding Method for Improving the Quantized SNR of SAR Raw Data
Sensors 2018, 18(12), 4221; https://doi.org/10.3390/s18124221
Received: 23 October 2018 / Revised: 21 November 2018 / Accepted: 26 November 2018 / Published: 1 December 2018
PDF Full-text (4843 KB) | HTML Full-text | XML Full-text
Abstract
When the original echo data of SAR are saturated for quantization, the performance of the commonly used block adaptive quantization (BAQ) algorithm will be degraded, which will degrade the imaging quality. This article proposes an improved Llody-Max codec method, which only needs to
[...] Read more.
When the original echo data of SAR are saturated for quantization, the performance of the commonly used block adaptive quantization (BAQ) algorithm will be degraded, which will degrade the imaging quality. This article proposes an improved Llody-Max codec method, which only needs to change the codec look-up table to get better quantization performance when the original echo is saturated. The simulation results show that the proposed method can reduce the quantization power loss, improve the echo signal-to-noise ratio (SNR), and reduce the influence of quantization saturation on the scattering mechanism of polarized SAR data, which have good practical application value. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle A Crop Classification Method Integrating GF-3 PolSAR and Sentinel-2A Optical Data in the Dongting Lake Basin
Sensors 2018, 18(9), 3139; https://doi.org/10.3390/s18093139
Received: 30 June 2018 / Revised: 6 September 2018 / Accepted: 14 September 2018 / Published: 17 September 2018
PDF Full-text (15285 KB) | HTML Full-text | XML Full-text
Abstract
With the increasing of satellite sensors, more available multi-source data can be used for large-scale high-precision crop classification. Both polarimetric synthetic aperture radar (PolSAR) and multi-spectral optical data have been widely used for classification. However, it is difficult to combine the covariance matrix
[...] Read more.
With the increasing of satellite sensors, more available multi-source data can be used for large-scale high-precision crop classification. Both polarimetric synthetic aperture radar (PolSAR) and multi-spectral optical data have been widely used for classification. However, it is difficult to combine the covariance matrix of PolSAR data with the spectral bands of optical data. Using Hoekman’s method, this study solves the above problems by transforming the covariance matrix to an intensity vector that includes multiple intensity values on different polarization basis. In order to reduce the features redundancy, the principal component analysis (PCA) algorithm is adopted to select some useful polarimetric and optical features. In this study, the PolSAR data acquired by satellite Gaofen-3 (GF-3) on 19 July 2017 and the optical data acquired by Sentinel-2A on 17 July 2017 over the Dongting lake basin are selected for the validation experiment. The results show that the full feature integration method proposed in this study achieves an overall classification accuracy of 85.27%, higher than that of the single dataset method or some other feature integration modes. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle A Channel Phase Error Correction Method Based on Joint Quality Function of GF-3 SAR Dual-Channel Images
Sensors 2018, 18(9), 3131; https://doi.org/10.3390/s18093131
Received: 30 June 2018 / Revised: 2 September 2018 / Accepted: 4 September 2018 / Published: 17 September 2018
PDF Full-text (2363 KB) | HTML Full-text | XML Full-text
Abstract
Multichannel SAR is an effective approach to solving the contradiction between high azimuth resolution and wide swath. The goal of this paper is to obtain a new and effective method for estimating and compensating the interchannel phase error of the Chinese GF-3 Synthetic
[...] Read more.
Multichannel SAR is an effective approach to solving the contradiction between high azimuth resolution and wide swath. The goal of this paper is to obtain a new and effective method for estimating and compensating the interchannel phase error of the Chinese GF-3 Synthetic aperture radar (SAR). A channel phase error correction method based on the optimal value of the image domain quality function is proposed. In this method, the phase error is initially compensated using the correlation function method. In the fine correction of dual-channel phase error, a heuristic search algorithm is used to estimate the residual phase by searching the extremum of the quality function. After phase compensation in the image domain, the azimuth ambiguities caused by the remaining phase are eliminated. The proposed image domain processing method provides a new idea for channel phase error correction. The measured data of high-resolution GF-3 dual-channel ultrafine imaging mode verifies the validity of this method. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks
Sensors 2018, 18(9), 2915; https://doi.org/10.3390/s18092915
Received: 26 June 2018 / Revised: 23 August 2018 / Accepted: 31 August 2018 / Published: 2 September 2018
PDF Full-text (35796 KB) | HTML Full-text | XML Full-text
Abstract
Emergency flood monitoring and rescue need to first detect flood areas. This paper provides a fast and novel flood detection method and applies it to Gaofen-3 SAR images. The fully convolutional network (FCN), a variant of VGG16, is utilized for flood mapping in
[...] Read more.
Emergency flood monitoring and rescue need to first detect flood areas. This paper provides a fast and novel flood detection method and applies it to Gaofen-3 SAR images. The fully convolutional network (FCN), a variant of VGG16, is utilized for flood mapping in this paper. Considering the requirement of flood detection, we fine-tune the model to get higher accuracy results with shorter training time and fewer training samples. Compared with state-of-the-art methods, our proposed algorithm not only gives robust and accurate detection results but also significantly reduces the detection time. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Soil Moisture Retrieval from the Chinese GF-3 Satellite and Optical Data over Agricultural Fields
Sensors 2018, 18(8), 2675; https://doi.org/10.3390/s18082675
Received: 1 August 2018 / Revised: 11 August 2018 / Accepted: 13 August 2018 / Published: 14 August 2018
PDF Full-text (16713 KB) | HTML Full-text | XML Full-text
Abstract
Timely and accurate soil moisture information is of great importance in agricultural monitoring. The Gaofen-3 (GF-3) satellite, the first C-band multi-polarization synthetic-aperture radar (SAR) satellite in China, provides valuable data sources for soil moisture monitoring. In this study, a soil moisture retrieval algorithm
[...] Read more.
Timely and accurate soil moisture information is of great importance in agricultural monitoring. The Gaofen-3 (GF-3) satellite, the first C-band multi-polarization synthetic-aperture radar (SAR) satellite in China, provides valuable data sources for soil moisture monitoring. In this study, a soil moisture retrieval algorithm was developed for the GF-3 satellite based on a backscattering coefficient simulation database. We adopted eight optical vegetation indices to determine the relationships between these indices and vegetation water content (VWC) by combining Landsat-8 data and field measurements. A backscattering coefficient database was built using an advanced integral equation model (AIEM). The effects of vegetation on backscattering coefficients were corrected using the water cloud model (WCM) to obtain the bare soil backscattering coefficient ( σ s o i l ° ). Then, soil moisture retrievals were obtained at HH, VV and HH+VV combination respectively by minimizing the observed bare soil backscattering coefficient ( σ s o i l ° ) and the AIEM-simulated backscattering coefficient ( σ soil-simu ° ). Finally, the proposed algorithm was validated in agriculture region of wheat and corn in China using ground soil moisture measurements. The results showed that the normalized difference infrared index (NDII) had the best fit with measured VWC values (R = 0.885) among the eight vegetation water indices; thus, it was adopted to correct the effects of vegetation. The proposed algorithm using GF-3 satellite data performed well in soil moisture retrieval, and the scheme combining HH and VV polarization exhibited the highest accuracy, with a root mean square error (RMSE) of 0.044 m3m−3, followed by HH polarization (RMSE = 0.049 m3m−3) and VV polarization (RMSE = 0.053 m3m−3). Therefore, the proposed algorithm has good potential to operationally estimate soil moisture from the new GF-3 satellite data. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle An Accurate Measurement Method for Azimuth Pointing of Spaceborne Synthetic Aperture Radar Antenna Beams Based on Ground Receiver
Sensors 2018, 18(8), 2626; https://doi.org/10.3390/s18082626
Received: 29 June 2018 / Revised: 27 July 2018 / Accepted: 8 August 2018 / Published: 10 August 2018
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Abstract
The paper proposes a new method for measuring the azimuth pointing of spaceborne synthetic aperture radar (SAR) antenna beams based on the ground receiver, which can receive and record complex sampling data of the pulse signals transmitted from the spaceborne SAR. The center
[...] Read more.
The paper proposes a new method for measuring the azimuth pointing of spaceborne synthetic aperture radar (SAR) antenna beams based on the ground receiver, which can receive and record complex sampling data of the pulse signals transmitted from the spaceborne SAR. The center of the antenna pattern is extracted from the complex sampling data amplitude envelope to obtain the time when the beam main lobe center irradiates the ground receiver, and the range migration information is extracted from the complex sampling data to obtain the time when the satellite is over the top of the ground receiver. The results of Chinese civilian remote sensing GaoFen-3 SAR satellite experiment data processing show that the measurement accuracy of this method is better than 0.002°, which can be applied to the accurate measurement of azimuth pointing of various low Earth orbit (LEO) SAR antenna beams. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Design and Implementation of a Novel Polarimetric Active Radar Calibrator for Gaofen-3 SAR
Sensors 2018, 18(8), 2620; https://doi.org/10.3390/s18082620
Received: 26 June 2018 / Revised: 7 August 2018 / Accepted: 8 August 2018 / Published: 10 August 2018
PDF Full-text (2340 KB) | HTML Full-text | XML Full-text
Abstract
The Chinese first fully polarimetric space-borne synthetic aperture radar (SAR)-Gaofen-3 (GF-3) was launched in August 2016, which operates at the C-band and the resolution can reach 1 m. Polarimetric SAR calibration is a procedure that corrects the polarization distortion of a measured scattering
[...] Read more.
The Chinese first fully polarimetric space-borne synthetic aperture radar (SAR)-Gaofen-3 (GF-3) was launched in August 2016, which operates at the C-band and the resolution can reach 1 m. Polarimetric SAR calibration is a procedure that corrects the polarization distortion of a measured scattering matrix by referring to the scattering matrix of a known target. The present paper describes the principle, design, manufacture, and measurement results of a novel polarimetric active radar calibrator (PARC) designed for GF-3. A new design method for PARC was presented and two dual-polarized antennas with very high polarization purity were used. The internal calibration technique was introduced to ensure balance in the amplitude and phase, which ensures the precision of the PARC’s scattering matrices. The results we obtained through measurement in the microwave anechoic chamber and experiments in in-orbit calibration agree well with the theoretical predictions, and the novel PARC presented is proved to be well suited for polarization and radiometric calibration of GF-3. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle A Real-Time Imaging Algorithm Based on Sub-Aperture CS-Dechirp for GF3-SAR Data
Sensors 2018, 18(8), 2562; https://doi.org/10.3390/s18082562
Received: 30 June 2018 / Revised: 29 July 2018 / Accepted: 2 August 2018 / Published: 5 August 2018
Cited by 1 | PDF Full-text (7581 KB) | HTML Full-text | XML Full-text
Abstract
Conventional synthetic aperture radar (SAR) imaging algorithms usually require a period of time to process data that is longer than the time it takes to record one synthetic aperture or that corresponding to an adequate azimuth resolution. That is to say, the real-time
[...] Read more.
Conventional synthetic aperture radar (SAR) imaging algorithms usually require a period of time to process data that is longer than the time it takes to record one synthetic aperture or that corresponding to an adequate azimuth resolution. That is to say, the real-time processing system is idle during the long data recording time and the utilization of computational resources is low. To deal with this problem, a real-time imaging algorithm based on sub-aperture chirp scaling dechirp (CS-dechirp) is proposed in this paper. With CS-dechirp, the sub-aperture data could be processed to form an image with relatively low resolution. Subsequently, a few low-resolution images are generated as longer azimuth data are recorded. At the stage of full-resolution image generation, a coherent combination method for the low-resolution complex-value images is developed. As the low-resolution complex-value images are coherently combined one by one, the resolution is gradually improved and the full-resolution image is finally obtained. The results of a simulation and real data from the GF3-SAR validate the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Geo-Positioning Accuracy Improvement of Multi-Mode GF-3 Satellite SAR Imagery Based on Error Sources Analysis
Sensors 2018, 18(7), 2333; https://doi.org/10.3390/s18072333
Received: 15 June 2018 / Revised: 14 July 2018 / Accepted: 16 July 2018 / Published: 18 July 2018
Cited by 1 | PDF Full-text (2756 KB) | HTML Full-text | XML Full-text
Abstract
The GaoFen-3 (GF-3) satellite is the only synthetic aperture radar (SAR) satellite in the High-Resolution Earth Observation System Project, which is the first C-band full-polarization SAR satellite in China. In this paper, we proposed some error sources-based weight strategies to improve the geometric
[...] Read more.
The GaoFen-3 (GF-3) satellite is the only synthetic aperture radar (SAR) satellite in the High-Resolution Earth Observation System Project, which is the first C-band full-polarization SAR satellite in China. In this paper, we proposed some error sources-based weight strategies to improve the geometric performance of multi-mode GF-3 satellite SAR images without using ground control points (GCPs). To get enough tie points, a robust SAR image registration method and the SAR-features from accelerated segment test (SAR-FAST) method is used to achieve the image registration and tie point extraction. Then, the original position of these tie points in object-space is calculated with the help of the space intersection method. With the dataset clustered by the density-based spatial clustering of applications with noise (DBSCAN) algorithm, we undertake the block adjustment with a bias-compensated rational function model (RFM) aided to improve the geometric performance of these multi-mode GF-3 satellite SAR images. Different weight strategies are proposed to develop the normal equation matrix according to the error sources analysis of GF-3 satellite SAR images, and the preconditioned conjugate gradient (PCG) method is utilized to solve the normal equation. The experimental results indicate that our proposed method can improve the geometric positioning accuracy of GF-3 satellite SAR images within 2 pixels. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle A PolSAR Image Segmentation Algorithm Based on Scattering Characteristics and the Revised Wishart Distance
Sensors 2018, 18(7), 2262; https://doi.org/10.3390/s18072262
Received: 21 May 2018 / Revised: 8 July 2018 / Accepted: 10 July 2018 / Published: 13 July 2018
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Abstract
A novel segmentation algorithm for polarimetric synthetic aperture radar (PolSAR) images is proposed in this paper. The method is composed of two essential components: a merging order and a merging predicate. The similarity measured by the complex-kind Hotelling–Lawley trace (HLT) statistic is used
[...] Read more.
A novel segmentation algorithm for polarimetric synthetic aperture radar (PolSAR) images is proposed in this paper. The method is composed of two essential components: a merging order and a merging predicate. The similarity measured by the complex-kind Hotelling–Lawley trace (HLT) statistic is used to decide the merging order. The merging predicate is determined by the scattering characteristics and the revised Wishart distance between adjacent pixels, which can greatly improve the performance in speckle suppression and detail preservation. A postprocessing step is applied to obtain a satisfactory result after the merging operation. The decomposition and merging processes are iteratively executed until the termination criterion is met. The superiority of the proposed method was verified with experiments on two RADARSAT-2 PolSAR images and a Gaofen-3 PolSAR image, which demonstrated that the proposed method can obtain more accurate segmentation results and shows a better performance in speckle suppression and detail preservation than the other algorithms. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Decimeter-Level Geolocation Accuracy Updated by a Parametric Tropospheric Model with GF-3
Sensors 2018, 18(7), 2197; https://doi.org/10.3390/s18072197
Received: 22 May 2018 / Revised: 3 July 2018 / Accepted: 6 July 2018 / Published: 8 July 2018
PDF Full-text (2642 KB) | HTML Full-text | XML Full-text
Abstract
GaoFen-3 (GF-3) is a multi-polarization C-band synthetic aperture radar (SAR) satellite in China with a resolution of up to 1 m. Up to now, the geolocation accuracy of GF-3 could be improved to 3 m. According to the current study, there still exist
[...] Read more.
GaoFen-3 (GF-3) is a multi-polarization C-band synthetic aperture radar (SAR) satellite in China with a resolution of up to 1 m. Up to now, the geolocation accuracy of GF-3 could be improved to 3 m. According to the current study, there still exist meter-level geolocation residuals caused by atmospheric path delay after compensating with a static tropospheric model. In this paper, we compensate the residuals with the sophisticated tropospheric model based on real meteorological data. The experimental results show that the tropospheric model has an accuracy on the millimeter level, which can increase GF-3’s geolocation accuracy to several decimeters compared with the static tropospheric model. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle InSAR Baseline Estimation for Gaofen-3 Real-Time DEM Generation
Sensors 2018, 18(7), 2152; https://doi.org/10.3390/s18072152
Received: 25 May 2018 / Revised: 29 June 2018 / Accepted: 1 July 2018 / Published: 4 July 2018
PDF Full-text (10484 KB) | HTML Full-text | XML Full-text
Abstract
For Interferometry Synthetic Aperture Radar (InSAR), the normal baseline is one of the main factors that affects the accuracy of the ground elevation. For Gaofen-3 (GF-3) InSAR processing, the poor accuracy of the real-time orbit determination results in a large baseline error, leads
[...] Read more.
For Interferometry Synthetic Aperture Radar (InSAR), the normal baseline is one of the main factors that affects the accuracy of the ground elevation. For Gaofen-3 (GF-3) InSAR processing, the poor accuracy of the real-time orbit determination results in a large baseline error, leads to a modulation error in azimuth and a slope error in the range for timely Digital Elevation Model (DEM) generation. In order to address this problem, a novel baseline estimation approach based on Shuttle Radar Topography Mission (SRTM) DEM is proposed in this paper. Firstly, the orbit fitting is executed to remove the non-linear error factor, which is different from traditional methods. Secondly, the height errors are obtained in a slant-range plane between SRTM DEM and the GF-3 generated DEM, which can be used to estimate the baseline error with a linear variation. Then, the real-time orbit can be calibrated by the baseline error. Finally, the DEM generation is performed by using the modified baseline and orbit. This approach has the merit of spatial and precise orbital free ability. Based on the results of GF-3 interferometric SAR data for Hebei, the effectiveness of the proposed algorithm is verified and the accuracy of GF-3 real-time DEM products can be improved extensively. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle A Promising Method of Typhoon Wave Retrieval from Gaofen-3 Synthetic Aperture Radar Image in VV-Polarization
Sensors 2018, 18(7), 2064; https://doi.org/10.3390/s18072064
Received: 13 April 2018 / Revised: 11 June 2018 / Accepted: 26 June 2018 / Published: 28 June 2018
Cited by 1 | PDF Full-text (8307 KB) | HTML Full-text | XML Full-text
Abstract
The motivation of this work is to explore the possibility of typhoon wave retrieval (the main parameter is significant wave height (SWH)) for C-band Gaofen (GF-3) synthetic aperture radar (SAR) with a wide swath coverage (>400 km). We aim to establish an analysis
[...] Read more.
The motivation of this work is to explore the possibility of typhoon wave retrieval (the main parameter is significant wave height (SWH)) for C-band Gaofen (GF-3) synthetic aperture radar (SAR) with a wide swath coverage (>400 km). We aim to establish an analysis of a typhoon wave in the subresolution-scale (approximately 20 × 20 km2) on GF-3 SAR through SAR-measured parameters, including a normalized radar cross section (NRCS) and variance of the normalized SAR image (herein called cvar), which are the basic variables in an empirical wave retrieval algorithm and are independent of visible wave streaks. Several typhoons around the China Seas were captured by Chinese GF-3 SAR in 2017; e.g., Noru, Doksuri, Talim and Hato. The wave fields simulated from the third-generation numerical wave model WAVEWATCH-III (WW3) are collocated with these images. In general, the distribution patterns of the typhoon waves from the WW3 model are consistent with wave fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) at 0.125° grids, indicating that the simulation results from the WW3 model are suitable for our study. In addition to winds retrieved from GF-3 SAR images in vertical-horizontal (VH) polarization, the characteristics of the typhoon wave on vertical-vertical (VV) polarization GF-3 SAR images are studied. It is found that SWH has a linear relationship with NRCS and cvar, however, SWH fluctuates with wind speed at all incidence angles. Based on the analyzed results, we simply tune two empirical wave retrieval algorithms for GF-3 SAR in typhoons. Although the correlation (COR) reaches 0.5 taking account into the NRCS term, a more accurate retrieval algorithm, including more related terms, is anticipated for further development for GF-3 SAR and validated through more typhoon images. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Land Cover Classification with GF-3 Polarimetric Synthetic Aperture Radar Data by Random Forest Classifier and Fast Super-Pixel Segmentation
Sensors 2018, 18(7), 2014; https://doi.org/10.3390/s18072014
Received: 5 March 2018 / Revised: 12 June 2018 / Accepted: 19 June 2018 / Published: 22 June 2018
Cited by 2 | PDF Full-text (25247 KB) | HTML Full-text | XML Full-text
Abstract
Chinese Gaofen-3 (GF-3), a vital satellite for high-resolution earth observation, was the first C-band polarimetric synthetic aperture radar (SAR) launched in China with a resolution of up to one meter. Polarimetric SAR can obtain the complete physical scattering mechanisms of targets, thereby having
[...] Read more.
Chinese Gaofen-3 (GF-3), a vital satellite for high-resolution earth observation, was the first C-band polarimetric synthetic aperture radar (SAR) launched in China with a resolution of up to one meter. Polarimetric SAR can obtain the complete physical scattering mechanisms of targets, thereby having the potential to differentiate objects. In this paper, several classification methods are briefly summarized and the types of features that should be chosen during classification are discussed. A pre-classification step is introduced to reduce the workload of precise labeling. The Random Forest classifier, which performs well for many other classification tasks, is used for the initial land cover classification. Then, based on a polarimetric constant false-alarm rate (CFAR) edge detector, a fast super-pixel generation method for polarimetric SAR image is proposed, which does not require the adjustment of parameters in advance. Following that, majority vote is conducted on the initial classification result based on the super-pixels, so that the classification result can be optimized to better meet the mapping requirements. The experimental results based on GF-3 polarimetric SAR data verify the effectiveness of proposed procedure and demonstrate that GF-3 data has excellent performance in land cover classification. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Coastline Detection with Gaofen-3 SAR Images Using an Improved FCM Method
Sensors 2018, 18(6), 1898; https://doi.org/10.3390/s18061898
Received: 28 March 2018 / Revised: 23 May 2018 / Accepted: 28 May 2018 / Published: 11 June 2018
PDF Full-text (6702 KB) | HTML Full-text | XML Full-text
Abstract
The coastline detection is one of the main applications of the Gaofen-3 satellite in the ocean field. However, the capability of Gaofen-3 SAR image in coastline detection has not yet been validated. In this paper, two Gaofen-3 SAR images, acquired in 2016, were
[...] Read more.
The coastline detection is one of the main applications of the Gaofen-3 satellite in the ocean field. However, the capability of Gaofen-3 SAR image in coastline detection has not yet been validated. In this paper, two Gaofen-3 SAR images, acquired in 2016, were used to extract the coastlines of the regions of Bohai and Taihu in China, respectively. The classical Fuzzy C-means (FCM) method was used in the coastline detection, but had been improved by combining the Wavelet decomposition algorithm to better suppress the inherent speckle noises of SAR image. Coastline detection results obtained from two Sentinel-1 SAR images acquired on the same regions were compared with those of the Gaofen-3 images. By using the manually delineated coastlines as the standards in the qualitative evaluations, improvements of about 12.0%, 8.3%, 23.8%, and 9.4% can be achieved by the improved FCM method with respect to the indicators of mean, RMSE, PGSD, and P90%, respectively; demonstrating that the Gaofen-3 data is superior to the Sentinel-1 data in the detection of coastline. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Adaptive Unscented Kalman Filter Phase Unwrapping Method and Its Application on Gaofen-3 Interferometric SAR Data
Sensors 2018, 18(6), 1793; https://doi.org/10.3390/s18061793
Received: 14 April 2018 / Revised: 19 May 2018 / Accepted: 30 May 2018 / Published: 2 June 2018
Cited by 1 | PDF Full-text (5839 KB) | HTML Full-text | XML Full-text
Abstract
Phase unwrapping (PU) is a key step in the reconstruction of digital elevation models (DEMs) and the monitoring of surface deformation from interferometric synthetic aperture radar (SAR, InSAR) data. In this paper, an improved PU method that combines an amended matrix pencil model,
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Phase unwrapping (PU) is a key step in the reconstruction of digital elevation models (DEMs) and the monitoring of surface deformation from interferometric synthetic aperture radar (SAR, InSAR) data. In this paper, an improved PU method that combines an amended matrix pencil model, an adaptive unscented kalman filter (AUKF), an efficient quality-guided strategy based on heapsort, and a circular median filter is proposed. PU theory and the existing UKFPU method are covered. Then, the improved method is presented with emphasis on the AUKF and the circular median filter. AUKF has been well used in other fields, but it is for the first time applied to interferometric images PU, to the best of our knowledge. First, the amended matrix pencil model is used to estimate the phase gradient. Then, an AUKF model is used to unwrap the interferometric phase based on an efficient quality-guided strategy based on heapsort. Finally, the key results are obtained by filtering the results using a circular median. The proposed method is compared with the minimum cost network flow (MCF), statistical cost network flow (SNAPHU), regularized phase tracking technique (RPTPU), and UKFPU methods using two sets of simulated data and two sets of experimental GF-3 SAR data. The improved method is shown to yield the greatest accuracy in the interferometric phase maps compared to the methods considered in this paper. Furthermore, the improved method is shown to be the most robust to noise and is thus most suitable for PU of GF-3 SAR data in high-noise and low-coherence regions. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle A Preliminary Analysis of Wind Retrieval, Based on GF-3 Wave Mode Data
Sensors 2018, 18(5), 1604; https://doi.org/10.3390/s18051604
Received: 16 April 2018 / Revised: 9 May 2018 / Accepted: 16 May 2018 / Published: 17 May 2018
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Abstract
This paper presents an analysis of measurements of the normalized radar cross-(NRCS) in Wave Mode for Chinese C-band Gaofen-3(GF-3) synthetic aperture radar (SAR). Based on 2779 images from GF-3 quad-polarization SAR in Wave Mode and collocated wind vectors from ERA-Interim, this experiment verifies
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This paper presents an analysis of measurements of the normalized radar cross-(NRCS) in Wave Mode for Chinese C-band Gaofen-3(GF-3) synthetic aperture radar (SAR). Based on 2779 images from GF-3 quad-polarization SAR in Wave Mode and collocated wind vectors from ERA-Interim, this experiment verifies the feasibility of using ocean surface wind fields and VV-polarized NRCS to perform normalized calibration. The method uses well-validated empirical C-band geophysical model function (CMOD4) to estimate the calibration constant for each beam. In addition, the relationship between cross-pol NRCS and wind vectors is discussed. The cross-pol NRCS increases linearly with wind speed and it is obviously modulated by the wind direction when the wind speed is greater than 8 m/s. Furthermore, the properties of the polarization ratio, denoted PR, are also investigated. The PR is dependent on incidence angle and azimuth angle. Two empirical models of the PR are fitted, one as a function of incidence angle only, the other with additional dependence on azimuth angle. Assessments show that the σ VV 0 retrieved from new PR models as well as σ HH 0 is in good agreement with σ VV 0 extracted from SAR images directly. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Sidelobe Suppression with Resolution Maintenance for SAR Images via Sparse Representation
Sensors 2018, 18(5), 1589; https://doi.org/10.3390/s18051589
Received: 4 April 2018 / Revised: 10 May 2018 / Accepted: 14 May 2018 / Published: 16 May 2018
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Abstract
Severe sidelobe interference is one of the major problems with traditional Synthetic Aperture Radar (SAR) imaging. In the observation scene of sea areas, the number of targets in the observation scene is so small that targets can be regarded as sparse. Taking this
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Severe sidelobe interference is one of the major problems with traditional Synthetic Aperture Radar (SAR) imaging. In the observation scene of sea areas, the number of targets in the observation scene is so small that targets can be regarded as sparse. Taking this into account, a method of sidelobe suppression, on the basis of sparsity constraint regularization, is proposed to reduce sidelobes of Gaofen-3 (GF-3) images in sea areas of the image domain. This proposed method has a prominent sidelobe suppression effect with resolution maintenance and without destruction of amplitude and phase information. This method can also be applied to SAR images of other satellites. In addition to the employment of peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR) in evaluating sidelobe suppression level, AE (amplitude error) and PE (phase error) are firstly defined for the evaluation of amplitude and phase-preserving quality, respectively. Through the proposed method, AE and PE values are nearly unchanged and the PSLR and ISLR are significantly reduced. The method, as an important part of the quality-improvement project of GF-3, has been successfully applied to the sidelobe suppression of GF-3 data. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Speckle Filtering of GF-3 Polarimetric SAR Data with Joint Restriction Principle
Sensors 2018, 18(5), 1533; https://doi.org/10.3390/s18051533
Received: 9 April 2018 / Revised: 8 May 2018 / Accepted: 9 May 2018 / Published: 12 May 2018
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Abstract
Polarimetric SAR (PolSAR) scattering characteristics of imagery are always obtained from the second order moments estimation of multi-polarization data, that is, the estimation of covariance or coherency matrices. Due to the extra-paths that signal reflected from separate scatterers within the resolution cell has
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Polarimetric SAR (PolSAR) scattering characteristics of imagery are always obtained from the second order moments estimation of multi-polarization data, that is, the estimation of covariance or coherency matrices. Due to the extra-paths that signal reflected from separate scatterers within the resolution cell has to travel, speckle noise always exists in SAR images and has a severe impact on the scattering performance, especially on single look complex images. In order to achieve high accuracy in estimating covariance or coherency matrices, three aspects are taken into consideration: (1) the edges and texture of the scene are distinct after speckle filtering; (2) the statistical characteristic should be similar to the object pixel; and (3) the polarimetric scattering signature should be preserved, in addition to speckle reduction. In this paper, a joint restriction principle is proposed to meet the requirement. Three different restriction principles are introduced to the processing of speckle filtering. First, a new template, which is more suitable for the point or line targets, is designed to ensure the morphological consistency. Then, the extent sigma filter is used to restrict the pixels in the template aforementioned to have an identical statistic characteristic. At last, a polarimetric similarity factor is applied to the same pixels above, to guarantee the similar polarimetric features amongst the optional pixels. This processing procedure is named as speckle filtering with joint restriction principle and the approach is applied to GF-3 polarimetric SAR data acquired in San Francisco, CA, USA. Its effectiveness of keeping the image sharpness and preserving the scattering mechanism as well as speckle reduction is validated by the comparison with boxcar filters and refined Lee filter. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle A Range Ambiguity Suppression Processing Method for Spaceborne SAR with Up and Down Chirp Modulation
Sensors 2018, 18(5), 1454; https://doi.org/10.3390/s18051454
Received: 12 March 2018 / Revised: 30 April 2018 / Accepted: 4 May 2018 / Published: 7 May 2018
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Abstract
Range ambiguity is one of the factors which affect the SAR image quality. Alternately transmitting up and down chirp modulation pulses is one of the methods used to suppress the range ambiguity. However, the defocusing range ambiguous signal can still hold the stronger
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Range ambiguity is one of the factors which affect the SAR image quality. Alternately transmitting up and down chirp modulation pulses is one of the methods used to suppress the range ambiguity. However, the defocusing range ambiguous signal can still hold the stronger backscattering intensity than the mainlobe imaging area in some case, which has a severe impact on visual effects and subsequent applications. In this paper, a novel hybrid range ambiguity suppression method for up and down chirp modulation is proposed. The method can obtain the ambiguity area image and reduce the ambiguity signal power appropriately, by applying pulse compression using a contrary modulation rate and CFAR detecting method. The effectiveness and correctness of the approach is demonstrated by processing the archive images acquired by Chinese Gaofen-3 SAR sensor in full-polarization mode. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Water Detection in Urban Areas from GF-3
Sensors 2018, 18(4), 1299; https://doi.org/10.3390/s18041299
Received: 3 March 2018 / Revised: 17 April 2018 / Accepted: 20 April 2018 / Published: 23 April 2018
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Abstract
The rapid and accurate detection of urban water is critical for urban management, river detection, and flood disaster assessment. This study is devoted to detecting water by GaoFen-3 (GF-3) Synthetic Aperture Radar (SAR) images with high spatial resolution. There have been no effective
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The rapid and accurate detection of urban water is critical for urban management, river detection, and flood disaster assessment. This study is devoted to detecting water by GaoFen-3 (GF-3) Synthetic Aperture Radar (SAR) images with high spatial resolution. There have been no effective solutions that discriminate water and building shadows using a single SAR image in previous research. Inspired by the principle that every shadow has a corresponding building nearby, a new method is proposed in this study, whereby building shadows are removed depending on the correspondence of buildings and their shadows. The proposed method is demonstrated effective and efficient by experimental results on six GF-3 SAR images. The Receiver Operating Characteristic (ROC) curves of the water detection results indicate that the proposed method increases the Probability of Detection (PD) to 98.36% and decreases the Probability of False Alarm (PFA) to 1.91% compared with the thresholding method, where, at the same PFA level, the maximum PD of the thresholding method is 72.62% in all testing samples. The proposed method is capable of removing building shadows and detecting water with high precision in urban areas, which presents the great potential of high-spatial-resolution GF-3 images in terms of water resource management. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Research on Strong Clutter Suppression for Gaofen-3 Dual-Channel SAR/GMTI
Sensors 2018, 18(4), 978; https://doi.org/10.3390/s18040978
Received: 23 January 2018 / Revised: 27 February 2018 / Accepted: 22 March 2018 / Published: 26 March 2018
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Abstract
In spaceborne synthetic aperture radar (SAR), moving targets are almost buried in ground clutter due to the wide clutter Doppler spectrum and the restricted pulse repetition frequency (PRF), which increases the difficulty of moving target detection. Clutter suppression is one of the key
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In spaceborne synthetic aperture radar (SAR), moving targets are almost buried in ground clutter due to the wide clutter Doppler spectrum and the restricted pulse repetition frequency (PRF), which increases the difficulty of moving target detection. Clutter suppression is one of the key issues in the spaceborne SAR moving target indicator operation. In this paper, we describe the clutter suppression principle and analyze the influence of amplitude and phase error on clutter suppression. In the following, a novel dual-channel SAR clutter suppression algorithm is proposed, which is suitable for the Gaofen-3(GF-3) SAR sensor. The proposed algorithm consists of three technique steps, namely adaptive two-dimensional (2D) channel calibration, refined amplitude error correction and refined phase error correction. After channel error is corrected by these procedures, the clutter component, especially a strong clutter component, can be well suppressed. The validity of the proposed algorithm is verified by GF-3 SAR real data which demonstrates the ground moving-target indication (GMTI) capability of GF-3 SAR sensor. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle A High-Resolution SAR Focusing Experiment Based on GF-3 Staring Data
Sensors 2018, 18(4), 943; https://doi.org/10.3390/s18040943
Received: 30 December 2017 / Revised: 11 March 2018 / Accepted: 20 March 2018 / Published: 22 March 2018
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Abstract
Spotlight synthetic aperture radar (SAR) is a proven technique, which can provide high-resolution images as compared to those produced by traditional stripmap SAR. This paper addresses a high-resolution SAR focusing experiment based on Gaofen-3 satellite (GF-3) staring data with about 55 cm azimuth
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Spotlight synthetic aperture radar (SAR) is a proven technique, which can provide high-resolution images as compared to those produced by traditional stripmap SAR. This paper addresses a high-resolution SAR focusing experiment based on Gaofen-3 satellite (GF-3) staring data with about 55 cm azimuth resolution and 240 MHz range bandwidth. In staring spotlight (ST) mode, the antenna always illuminates the same scene on the ground, which can extend the synthetic aperture. Based on a two-step processing algorithm, some special aspects such as curved-orbit model error correction, stop-and-go correction, and antenna pattern demodulation must be considered in image focusing. We provide detailed descriptions of all these aspects and put forward corresponding solutions. Using these suggested methods directly in an imaging module without any modification for other data processing software can make the most of the existing ground data processor. Finally, actual data acquired in GF-3 ST mode is used to validate these methodologies, and a well-focused, high-resolution image is obtained as a result of this focusing experiment. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle The GF-3 SAR Data Processor
Sensors 2018, 18(3), 835; https://doi.org/10.3390/s18030835
Received: 18 January 2018 / Revised: 7 March 2018 / Accepted: 8 March 2018 / Published: 10 March 2018
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Abstract
The Gaofen-3 (GF-3) data processor was developed as a workstation-based GF-3 synthetic aperture radar (SAR) data processing system. The processor consists of two vital subsystems of the GF-3 ground segment, which are referred to as data ingesting subsystem (DIS) and product generation subsystem
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The Gaofen-3 (GF-3) data processor was developed as a workstation-based GF-3 synthetic aperture radar (SAR) data processing system. The processor consists of two vital subsystems of the GF-3 ground segment, which are referred to as data ingesting subsystem (DIS) and product generation subsystem (PGS). The primary purpose of DIS is to record and catalogue GF-3 raw data with a transferring format, and PGS is to produce slant range or geocoded imagery from the signal data. This paper presents a brief introduction of the GF-3 data processor, including descriptions of the system architecture, the processing algorithms and its output format. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle A Quality Assessment Method Based on Common Distributed Targets for GF-3 Polarimetric SAR Data
Sensors 2018, 18(3), 807; https://doi.org/10.3390/s18030807
Received: 30 December 2017 / Revised: 2 March 2018 / Accepted: 6 March 2018 / Published: 7 March 2018
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Abstract
The GaoFen-3 (GF-3) satellite, launched on 10 August 2016, is the first C-band polarimetric synthetic aperture radar (PolSAR) satellite in China. The PolSAR system of GF-3 can collect a significant wealth of information for geophysical research and applications. Being used for related applications,
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The GaoFen-3 (GF-3) satellite, launched on 10 August 2016, is the first C-band polarimetric synthetic aperture radar (PolSAR) satellite in China. The PolSAR system of GF-3 can collect a significant wealth of information for geophysical research and applications. Being used for related applications, GF-3 PolSAR images must be of good quality. It is necessary to evaluate the quality of polarimetric data and achieve the normalized quality monitoring during 8-year designed life of GF-3. In this study, a new quality assessment method of PolSAR data based on common distributed targets is proposed, and the performance of the method is analyzed by simulations and GF-3 experiments. We evaluate the quality of GF-3 PolSAR data by this method. Results suggest that GF-3 antenna is highly isolated, and the quality of calibrated data satisfies the requests of quantitative applications. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Multi-Pixel Simultaneous Classification of PolSAR Image Using Convolutional Neural Networks
Sensors 2018, 18(3), 769; https://doi.org/10.3390/s18030769
Received: 30 December 2017 / Revised: 21 February 2018 / Accepted: 21 February 2018 / Published: 3 March 2018
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Abstract
Convolutional neural networks (CNN) have achieved great success in the optical image processing field. Because of the excellent performance of CNN, more and more methods based on CNN are applied to polarimetric synthetic aperture radar (PolSAR) image classification. Most CNN-based PolSAR image classification
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Convolutional neural networks (CNN) have achieved great success in the optical image processing field. Because of the excellent performance of CNN, more and more methods based on CNN are applied to polarimetric synthetic aperture radar (PolSAR) image classification. Most CNN-based PolSAR image classification methods can only classify one pixel each time. Because all the pixels of a PolSAR image are classified independently, the inherent interrelation of different land covers is ignored. We use a fixed-feature-size CNN (FFS-CNN) to classify all pixels in a patch simultaneously. The proposed method has several advantages. First, FFS-CNN can classify all the pixels in a small patch simultaneously. When classifying a whole PolSAR image, it is faster than common CNNs. Second, FFS-CNN is trained to learn the interrelation of different land covers in a patch, so it can use the interrelation of land covers to improve the classification results. The experiments of FFS-CNN are evaluated on a Chinese Gaofen-3 PolSAR image and other two real PolSAR images. Experiment results show that FFS-CNN is comparable with the state-of-the-art PolSAR image classification methods. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Real-Time Spaceborne Synthetic Aperture Radar Float-Point Imaging System Using Optimized Mapping Methodology and a Multi-Node Parallel Accelerating Technique
Sensors 2018, 18(3), 725; https://doi.org/10.3390/s18030725
Received: 28 December 2017 / Revised: 31 January 2018 / Accepted: 5 February 2018 / Published: 28 February 2018
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Abstract
With the development of satellite load technology and very large-scale integrated (VLSI) circuit technology, on-board real-time synthetic aperture radar (SAR) imaging systems have facilitated rapid response to disasters. A key goal of the on-board SAR imaging system design is to achieve high real-time
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With the development of satellite load technology and very large-scale integrated (VLSI) circuit technology, on-board real-time synthetic aperture radar (SAR) imaging systems have facilitated rapid response to disasters. A key goal of the on-board SAR imaging system design is to achieve high real-time processing performance under severe size, weight, and power consumption constraints. This paper presents a multi-node prototype system for real-time SAR imaging processing. We decompose the commonly used chirp scaling (CS) SAR imaging algorithm into two parts according to the computing features. The linearization and logic-memory optimum allocation methods are adopted to realize the nonlinear part in a reconfigurable structure, and the two-part bandwidth balance method is used to realize the linear part. Thus, float-point SAR imaging processing can be integrated into a single Field Programmable Gate Array (FPGA) chip instead of relying on distributed technologies. A single-processing node requires 10.6 s and consumes 17 W to focus on 25-km swath width, 5-m resolution stripmap SAR raw data with a granularity of 16,384 × 16,384. The design methodology of the multi-FPGA parallel accelerating system under the real-time principle is introduced. As a proof of concept, a prototype with four processing nodes and one master node is implemented using a Xilinx xc6vlx315t FPGA. The weight and volume of one single machine are 10 kg and 32 cm × 24 cm × 20 cm, respectively, and the power consumption is under 100 W. The real-time performance of the proposed design is demonstrated on Chinese Gaofen-3 stripmap continuous imaging. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle An Automatic and Novel SAR Image Registration Algorithm: A Case Study of the Chinese GF-3 Satellite
Sensors 2018, 18(2), 672; https://doi.org/10.3390/s18020672
Received: 14 December 2017 / Revised: 12 February 2018 / Accepted: 14 February 2018 / Published: 24 February 2018
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Abstract
The Chinese GF-3 satellite launched in August 2016 is a Synthetic Aperture Radar (SAR) satellite that has the largest number of imaging modes in the world. It achieves a free switch in the spotlight, stripmap, scanSAR, wave, global observation and other imaging modes.
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The Chinese GF-3 satellite launched in August 2016 is a Synthetic Aperture Radar (SAR) satellite that has the largest number of imaging modes in the world. It achieves a free switch in the spotlight, stripmap, scanSAR, wave, global observation and other imaging modes. In order to further utilize GF-3 SAR images, an automatic and fast image registration procedure needs to be done. In this paper, we propose a novel image registration technique for GF-3 images of different imaging modes. The proposed algorithm consists of two stages: coarse registration and fine registration. In the first stage, we combine an adaptive sampling method with the SAR-SIFT algorithm to efficiently eliminate obvious translation, rotation and scale differences between the reference and sensed images. In the second stage, uniformly-distributed control points are extracted, then the fast normalized cross-correlation of an improved phase congruency model is utilized as a new similarity metric to match the reference image and the coarse-registered image in a local search region. Moreover, a selection strategy is used to remove outliers. Experimental results on several GF-3 SAR images of different imaging modes show that the proposed algorithm gives a robust, efficient and precise registration performance, compared with other state-of-the-art algorithms for SAR image registration. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Gaofen-3 PolSAR Image Classification via XGBoost and Polarimetric Spatial Information
Sensors 2018, 18(2), 611; https://doi.org/10.3390/s18020611
Received: 31 December 2017 / Revised: 13 February 2018 / Accepted: 14 February 2018 / Published: 17 February 2018
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Abstract
The launch of the Chinese Gaofen-3 (GF-3) satellite will provide enough synthetic aperture radar (SAR) images with different imaging modes for land cover classification and other potential usages in the next few years. This paper aims to propose an efficient and practical classification
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The launch of the Chinese Gaofen-3 (GF-3) satellite will provide enough synthetic aperture radar (SAR) images with different imaging modes for land cover classification and other potential usages in the next few years. This paper aims to propose an efficient and practical classification framework for a GF-3 polarimetric SAR (PolSAR) image. The proposed classification framework consists of four simple parts including polarimetric feature extraction and stacking, the initial classification via XGBoost, superpixels generation by statistical region merging (SRM) based on Pauli RGB image, and a post-processing step to determine the label of a superpixel by modified majority voting. Fast initial classification via XGBoost and the incorporation of spatial information via a post-processing step through superpixel-based modified majority voting would potentially make the method efficient in practical use. Preliminary experimental results on real GF-3 PolSAR images and the AIRSAR Flevoland data set validate the efficacy and efficiency of the proposed classification framework. The results demonstrate that the quality of GF-3 PolSAR data is adequate enough for classification purpose. The results also show that the incorporation of spatial information is important for overall performance improvement. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle A Novel Ship Detection Method Based on Gradient and Integral Feature for Single-Polarization Synthetic Aperture Radar Imagery
Sensors 2018, 18(2), 563; https://doi.org/10.3390/s18020563
Received: 30 December 2017 / Revised: 3 February 2018 / Accepted: 8 February 2018 / Published: 12 February 2018
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Abstract
With the rapid development of remote sensing technologies, SAR satellites like China’s Gaofen-3 satellite have more imaging modes and higher resolution. With the availability of high-resolution SAR images, automatic ship target detection has become an important topic in maritime research. In this paper,
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With the rapid development of remote sensing technologies, SAR satellites like China’s Gaofen-3 satellite have more imaging modes and higher resolution. With the availability of high-resolution SAR images, automatic ship target detection has become an important topic in maritime research. In this paper, a novel ship detection method based on gradient and integral features is proposed. This method is mainly composed of three steps. First, in the preprocessing step, a filter is employed to smooth the clutters and the smoothing effect can be adaptive adjusted according to the statistics information of the sub-window. Thus, it can retain details while achieving noise suppression. Second, in the candidate area extraction, a sea-land segmentation method based on gradient enhancement is presented. The integral image method is employed to accelerate computation. Finally, in the ship target identification step, a feature extraction strategy based on Haar-like gradient information and a Radon transform is proposed. This strategy decreases the number of templates found in traditional Haar-like methods. Experiments were performed using Gaofen-3 single-polarization SAR images, and the results showed that the proposed method has high detection accuracy and rapid computational efficiency. In addition, this method has the potential for on-board processing. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle An Unsupervised Change Detection Method Using Time-Series of PolSAR Images from Radarsat-2 and GaoFen-3
Sensors 2018, 18(2), 559; https://doi.org/10.3390/s18020559
Received: 31 December 2017 / Revised: 30 January 2018 / Accepted: 6 February 2018 / Published: 12 February 2018
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Abstract
The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change
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The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change based on time-series data from different sensors. Firstly, the overall difference image of the time-series PolSAR is calculated by omnibus test statistics, and difference images between any two images in different times are acquired by Rj test statistics. Secondly, the difference images are segmented with a Generalized Statistical Region Merging (GSRM) algorithm which can suppress the effect of speckle noise. Generalized Gaussian Mixture Model (GGMM) is then used to obtain the time-series change detection maps in the final step of the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection using time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can not only detect the time-series change from different sensors, but it can also better suppress the influence of speckle noise and improve the overall accuracy and Kappa coefficient. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle An Improved Adaptive Received Beamforming for Nested Frequency Offset and Nested Array FDA-MIMO Radar
Sensors 2018, 18(2), 520; https://doi.org/10.3390/s18020520
Received: 25 December 2017 / Revised: 2 February 2018 / Accepted: 5 February 2018 / Published: 8 February 2018
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Abstract
For the conventional FDA-MIMO (frequency diversity array multiple-input-multiple-output) Radar with uniform frequency offset and uniform linear array, the DOFs (degrees of freedom) of the adaptive beamformer are limited by the number of elements. A better performance—for example, a better suppression for strong interferences
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For the conventional FDA-MIMO (frequency diversity array multiple-input-multiple-output) Radar with uniform frequency offset and uniform linear array, the DOFs (degrees of freedom) of the adaptive beamformer are limited by the number of elements. A better performance—for example, a better suppression for strong interferences and a more desirable trade-off between the main lobe and side lobe—can be achieved with a greater number of DOFs. In order to obtain larger DOFs, this paper researches the signal model of the FDA-MIMO Radar with nested frequency offset and nested array, then proposes an improved adaptive beamforming method that uses the augmented matrix instead of the covariance matrix to calculate the optimum weight vectors and can be used to improve the output performances of FDA-MIMO Radar with the same element number or reduce the element number while maintain the approximate output performances such as the received beampattern, the main lobe width, side lobe depths and the output SINR (signal-to-interference-noise ratio). The effectiveness of the proposed scheme is verified by simulations. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Research on Synthetic Aperture Radar Processing for the Spaceborne Sliding Spotlight Mode
Sensors 2018, 18(2), 455; https://doi.org/10.3390/s18020455
Received: 18 December 2017 / Revised: 18 January 2018 / Accepted: 30 January 2018 / Published: 3 February 2018
PDF Full-text (8073 KB) | HTML Full-text | XML Full-text
Abstract
Gaofen-3 (GF-3) is China’ first C-band multi-polarization synthetic aperture radar (SAR) satellite, which also provides the sliding spotlight mode for the first time. Sliding-spotlight mode is a novel mode to realize imaging with not only high resolution, but also wide swath. Several key
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Gaofen-3 (GF-3) is China’ first C-band multi-polarization synthetic aperture radar (SAR) satellite, which also provides the sliding spotlight mode for the first time. Sliding-spotlight mode is a novel mode to realize imaging with not only high resolution, but also wide swath. Several key technologies for sliding spotlight mode in spaceborne SAR with high resolution are investigated in this paper, mainly including the imaging parameters, the methods of velocity estimation and ambiguity elimination, and the imaging algorithms. Based on the chosen Convolution BackProjection (CBP) and PFA (Polar Format Algorithm) imaging algorithms, a fast implementation method of CBP and a modified PFA method suitable for sliding spotlight mode are proposed, and the processing flows are derived in detail. Finally, the algorithms are validated by simulations and measured data. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Development of Wind Speed Retrieval from Cross-Polarization Chinese Gaofen-3 Synthetic Aperture Radar in Typhoons
Sensors 2018, 18(2), 412; https://doi.org/10.3390/s18020412
Received: 25 December 2017 / Revised: 27 January 2018 / Accepted: 29 January 2018 / Published: 31 January 2018
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Abstract
The purpose of our work is to determine the feasibility and effectiveness of retrieving sea surface wind speeds from C-band cross-polarization (herein vertical-horizontal, VH) Chinese Gaofen-3 (GF-3) SAR images in typhoons. In this study, we have collected three GF-3 SAR images acquired in
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The purpose of our work is to determine the feasibility and effectiveness of retrieving sea surface wind speeds from C-band cross-polarization (herein vertical-horizontal, VH) Chinese Gaofen-3 (GF-3) SAR images in typhoons. In this study, we have collected three GF-3 SAR images acquired in Global Observation (GLO) and Wide ScanSAR (WSC) mode during the summer of 2017 from the China Sea, which includes the typhoons Noru, Doksuri and Talim. These images were collocated with wind simulations at 0.12° grids from a numeric model, called the Regional Assimilation and Prediction System-Typhoon model (GRAPES-TYM). Recent research shows that GRAPES-TYM has a good performance for typhoon simulation in the China Sea. Based on the dataset, the dependence of wind speed and of radar incidence angle on normalized radar cross (NRCS) of VH-polarization GF-3 SAR have been investigated, after which an empirical algorithm for wind speed retrieval from VH-polarization GF-3 SAR was tuned. An additional four VH-polarization GF-3 SAR images in three typhoons, Noru, Hato and Talim, were investigated in order to validate the proposed algorithm. SAR-derived winds were compared with measurements from Windsat winds at 0.25° grids with wind speeds up to 40 m/s, showing a 5.5 m/s root mean square error (RMSE) of wind speed and an improved RMSE of 5.1 m/s wind speed was achieved compared with the retrieval results validated against GRAPES-TYM winds. It is concluded that the proposed algorithm is a promising potential technique for strong wind retrieval from cross-polarization GF-3 SAR images without encountering a signal saturation problem. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Polarimetric Calibration and Quality Assessment of the GF-3 Satellite Images
Sensors 2018, 18(2), 403; https://doi.org/10.3390/s18020403
Received: 31 December 2017 / Revised: 25 January 2018 / Accepted: 26 January 2018 / Published: 30 January 2018
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Abstract
The GaoFen-3 (GF-3) satellite is the first fully polarimetric synthetic aperture radar (SAR) satellite designed for civil use in China. The satellite operates in the C-band and has 12 imaging modes for various applications. Three fully polarimetric SAR (PolSAR) imaging modes are provided
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The GaoFen-3 (GF-3) satellite is the first fully polarimetric synthetic aperture radar (SAR) satellite designed for civil use in China. The satellite operates in the C-band and has 12 imaging modes for various applications. Three fully polarimetric SAR (PolSAR) imaging modes are provided with a resolution of up to 8 m. Although polarimetric calibration (PolCAL) of the SAR system is periodically undertaken, there is still some residual distortion in the images. In order to assess the polarimetric accuracy of this satellite and improve the image quality, we analyzed the polarimetric distortion errors and performed a PolCAL experiment based on scattering properties and corner reflectors. The experiment indicates that the GF-3 images can meet the satellite’s polarimetric accuracy requirements, i.e., a channel imbalance of 0.5 dB in amplitude and ±10 degrees in phase and a crosstalk accuracy of −35 dB. However, some images still contain residual polarimetric distortion. The experiment also shows that the residual errors of the GF-3 standard images can be diminished after further PolCAL, with a channel imbalance of 0.26 dB in amplitude and ±0.2 degrees in phase and a crosstalk accuracy of −42 dB. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network
Sensors 2018, 18(2), 334; https://doi.org/10.3390/s18020334
Received: 11 December 2017 / Revised: 22 January 2018 / Accepted: 22 January 2018 / Published: 24 January 2018
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Abstract
Target detection is one of the important applications in the field of remote sensing. The Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) satellite launched by China is a powerful tool for maritime monitoring. This work aims at detecting ships in GF-3 SAR images using
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Target detection is one of the important applications in the field of remote sensing. The Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) satellite launched by China is a powerful tool for maritime monitoring. This work aims at detecting ships in GF-3 SAR images using a new land masking strategy, the appropriate model for sea clutter and a neural network as the discrimination scheme. Firstly, the fully convolutional network (FCN) is applied to separate the sea from the land. Then, by analyzing the sea clutter distribution in GF-3 SAR images, we choose the probability distribution model of Constant False Alarm Rate (CFAR) detector from K-distribution, Gamma distribution and Rayleigh distribution based on a tradeoff between the sea clutter modeling accuracy and the computational complexity. Furthermore, in order to better implement CFAR detection, we also use truncated statistic (TS) as a preprocessing scheme and iterative censoring scheme (ICS) for boosting the performance of detector. Finally, we employ a neural network to re-examine the results as the discrimination stage. Experiment results on three GF-3 SAR images verify the effectiveness and efficiency of this approach. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Sliding Spotlight Mode Imaging with GF-3 Spaceborne SAR Sensor
Sensors 2018, 18(1), 43; https://doi.org/10.3390/s18010043
Received: 1 November 2017 / Revised: 21 December 2017 / Accepted: 22 December 2017 / Published: 26 December 2017
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Abstract
Synthetic aperture radar (SAR) sliding spotlight work mode can achieve high resolutions and wide swath (HRWS) simultaneously by steering the radar antenna beam. This paper aims to obtain well focused images using sliding spotlight mode with the Chinese Gaofen-3 SAR sensor. We proposed
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Synthetic aperture radar (SAR) sliding spotlight work mode can achieve high resolutions and wide swath (HRWS) simultaneously by steering the radar antenna beam. This paper aims to obtain well focused images using sliding spotlight mode with the Chinese Gaofen-3 SAR sensor. We proposed an integrated imaging scheme with sliding spotlight echoes. In the imaging scheme, the two-step approach is applied to the spaceborne sliding spotlight SAR imaging algorithm, followed by the Doppler parameter estimation algorithm. The azimuth spectral folding phenomenon is overcome by the two-step approach. The results demonstrate a high Doppler parameter estimation accuracy. The proposed imaging process is accurate and highly efficient for sliding spotlight SAR mode. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Improvement of Gaofen-3 Absolute Positioning Accuracy Based on Cross-Calibration
Sensors 2017, 17(12), 2903; https://doi.org/10.3390/s17122903
Received: 29 October 2017 / Revised: 9 December 2017 / Accepted: 11 December 2017 / Published: 14 December 2017
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Abstract
The Chinese Gaofen-3 (GF-3) mission was launched in August 2016, equipped with a full polarimetric synthetic aperture radar (SAR) sensor in the C-band, with a resolution of up to 1 m. The absolute positioning accuracy of GF-3 is of great importance, and in-orbit
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The Chinese Gaofen-3 (GF-3) mission was launched in August 2016, equipped with a full polarimetric synthetic aperture radar (SAR) sensor in the C-band, with a resolution of up to 1 m. The absolute positioning accuracy of GF-3 is of great importance, and in-orbit geometric calibration is a key technology for improving absolute positioning accuracy. Conventional geometric calibration is used to accurately calibrate the geometric calibration parameters of the image (internal delay and azimuth shifts) using high-precision ground control data, which are highly dependent on the control data of the calibration field, but it remains costly and labor-intensive to monitor changes in GF-3’s geometric calibration parameters. Based on the positioning consistency constraint of the conjugate points, this study presents a geometric cross-calibration method for the rapid and accurate calibration of GF-3. The proposed method can accurately calibrate geometric calibration parameters without using corner reflectors and high-precision digital elevation models, thus improving absolute positioning accuracy of the GF-3 image. GF-3 images from multiple regions were collected to verify the absolute positioning accuracy after cross-calibration. The results show that this method can achieve a calibration accuracy as high as that achieved by the conventional field calibration method. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Assessment of GF-3 Polarimetric SAR Data for Physical Scattering Mechanism Analysis and Terrain Classification
Sensors 2017, 17(12), 2785; https://doi.org/10.3390/s17122785
Received: 11 October 2017 / Revised: 14 November 2017 / Accepted: 27 November 2017 / Published: 1 December 2017
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Abstract
On 10 August 2016 China launched the GF-3, its first C-band polarimetric synthetic aperture radar (SAR) satellite, which was put into operation at the end of January, 2017. GF-3 polarimetric SAR has many advantages such as high resolution and multi-polarization imaging capabilities. Polarimetric
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On 10 August 2016 China launched the GF-3, its first C-band polarimetric synthetic aperture radar (SAR) satellite, which was put into operation at the end of January, 2017. GF-3 polarimetric SAR has many advantages such as high resolution and multi-polarization imaging capabilities. Polarimetric SAR can fully characterize the backscatter property of targets, and thus it is of great interest to explore the physical scattering mechanisms of terrain types, which is very important in interpreting polarimetric SAR imagery and for its further usages in Earth observations. In this paper, focusing on target scattering characterization and feature extraction, we generalize the Δ α B / α B method, which was proposed under the reflection symmetric assumption, for the general backscatter process to account for both the reflection symmetry and asymmetry cases. Then, we evaluate the performances of physical scattering mechanism analysis methods for GF-3 polarimetric SAR imagery. Radarsat-2 data acquired over the same area is used for cross validation. Results show that GF-3 polarimetric SAR data has great potential for target characterization, especially for ocean area observation. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle First Spaceborne SAR-GMTI Experimental Results for the Chinese Gaofen-3 Dual-Channel SAR Sensor
Sensors 2017, 17(11), 2683; https://doi.org/10.3390/s17112683
Received: 3 September 2017 / Revised: 16 November 2017 / Accepted: 16 November 2017 / Published: 21 November 2017
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Abstract
In spaceborne synthetic aperture radar (SAR) sensors, it is a challenging task to detect ground slow-moving targets against strong clutter background with limited spatial channels and restricted pulse repetition frequency (PRF). In this paper, we evaluate the image-based dual-channel SAR-ground moving target indication
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In spaceborne synthetic aperture radar (SAR) sensors, it is a challenging task to detect ground slow-moving targets against strong clutter background with limited spatial channels and restricted pulse repetition frequency (PRF). In this paper, we evaluate the image-based dual-channel SAR-ground moving target indication (SAR-GMTI) workflow for the Gaofen-3 SAR sensor and analyze the impact of strong azimuth ambiguities on GMTI when the displaced phase center antenna (DPCA) condition is not fully satisfied, which has not been demonstrated yet. An effective sliding window design technique based on system parameters analysis is proposed to deal with azimuth ambiguities and reduce false alarm. In the SAR-GMTI experiments, co-registration, clutter suppression, constant false alarm rate (CFAR) detector, vector velocity estimation and moving target relocation are analyzed and discussed thoroughly. With the real measured data of the Gaofen-3 dual-channel SAR sensor, the GMTI capability of this sensor is demonstrated and the effectiveness of the proposed method is verified. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle The SAR Payload Design and Performance for the GF-3 Mission
Sensors 2017, 17(10), 2419; https://doi.org/10.3390/s17102419
Received: 31 August 2017 / Revised: 3 October 2017 / Accepted: 19 October 2017 / Published: 23 October 2017
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Abstract
This paper describes the C-band multi-polarization Synthetic Aperture Radar (SAR) sensor for the Gaofen-3 (GF-3) mission. Based on the requirement analysis, the design of working modes and SAR payload are given. An accurate antenna model is introduced for the pattern optimization and SAR
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This paper describes the C-band multi-polarization Synthetic Aperture Radar (SAR) sensor for the Gaofen-3 (GF-3) mission. Based on the requirement analysis, the design of working modes and SAR payload are given. An accurate antenna model is introduced for the pattern optimization and SAR performance calculation. The paper concludes with an overview of predicted performance which was verified by in-orbit tests. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Multi-Mode GF-3 Satellite Image Geometric Accuracy Verification Using the RPC Model
Sensors 2017, 17(9), 2005; https://doi.org/10.3390/s17092005
Received: 26 July 2017 / Revised: 26 August 2017 / Accepted: 26 August 2017 / Published: 1 September 2017
Cited by 7 | PDF Full-text (2252 KB) | HTML Full-text | XML Full-text
Abstract
The GaoFen-3 (GF-3) satellite is the first C-band multi-polarization synthetic aperture radar (SAR) imaging satellite with a resolution up to 1 m in China. It is also the only SAR satellite of the High-Resolution Earth Observation System designed for civilian use. There are
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The GaoFen-3 (GF-3) satellite is the first C-band multi-polarization synthetic aperture radar (SAR) imaging satellite with a resolution up to 1 m in China. It is also the only SAR satellite of the High-Resolution Earth Observation System designed for civilian use. There are 12 different imaging models to meet the needs of different industry users. However, to use SAR satellite images for related applications, they must possess high geometric accuracy. In order to verify the geometric accuracy achieved by the different modes of GF-3 images, we analyze the SAR geometric error source and perform geometric correction tests based on the RPC model with and without ground control points (GCPs) for five imaging modes. These include the spotlight (SL), ultra-fine strip (UFS), Fine Strip I (FSI), Full polarized Strip I (QPSI), and standard strip (SS) modes. Experimental results show that the check point residuals are large and consistent without GCPs, but the root mean square error of the independent checkpoints for the case of four corner control points is better than 1.5 pixels, achieving a similar level of geometric positioning accuracy to that of international satellites. We conclude that the GF-3 satellite can be used for high-accuracy geometric processing and related industry applications. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Geometric Calibration and Accuracy Verification of the GF-3 Satellite
Sensors 2017, 17(9), 1977; https://doi.org/10.3390/s17091977
Received: 3 August 2017 / Revised: 23 August 2017 / Accepted: 28 August 2017 / Published: 29 August 2017
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Abstract
The GF-3 satellite is the first multi-polarization synthetic aperture radar (SAR) imaging satellite in China, which operates in the C band with a resolution of 1 m. Although the SAR satellite system was geometrically calibrated during the in-orbit commissioning phase, there are still
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The GF-3 satellite is the first multi-polarization synthetic aperture radar (SAR) imaging satellite in China, which operates in the C band with a resolution of 1 m. Although the SAR satellite system was geometrically calibrated during the in-orbit commissioning phase, there are still some system errors that affect its geometric positioning accuracy. In this study, these errors are classified into three categories: fixed system error, time-varying system error, and random error. Using a multimode hybrid geometric calibration of spaceborne SAR, and considering the atmospheric propagation delay, all system errors can be effectively corrected through high-precision ground control points and global atmospheric reference data. The geometric calibration experiments and accuracy evaluation for the GF-3 satellite are performed using ground control data from several regions. The experimental results show that the residual system errors of the GF-3 SAR satellite have been effectively eliminated, and the geometric positioning accuracy can be better than 3 m. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Unambiguous Imaging of Static Scenes and Moving Targets with the First Chinese Dual-Channel Spaceborne SAR Sensor
Sensors 2017, 17(8), 1709; https://doi.org/10.3390/s17081709
Received: 17 June 2017 / Revised: 19 July 2017 / Accepted: 21 July 2017 / Published: 25 July 2017
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Abstract
Multichannel synthetic aperture radar (SAR) is a breakthrough given the inherent limitation between high-resolution and wide-swath (HRWS) faced with conventional SAR. This paper aims to obtain unambiguous imaging of static scenes and moving targets with the first Chinese dual-channel spaceborne SAR sensor. We
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Multichannel synthetic aperture radar (SAR) is a breakthrough given the inherent limitation between high-resolution and wide-swath (HRWS) faced with conventional SAR. This paper aims to obtain unambiguous imaging of static scenes and moving targets with the first Chinese dual-channel spaceborne SAR sensor. We propose an integrated imaging scheme with the dual-channel echoes. In the imaging scheme, the subspace-based error estimation algorithm is first applied to the spaceborne multichannel SAR system, followed by the reconstruction algorithm prior to imaging. The motion-adapted reconstruction algorithm for moving target imaging is initially achieved with the spaceborne multichannel SAR system. The results exhibit an effective suppression of azimuth ambiguities and false targets with the proposed process. This paper verifies the accuracy of the subspace-based channel error estimator and the feasibility of the motion-adapted reconstruction algorithm. The proposed imaging process has prospects for future HRWS SAR systems with more channels. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Preliminary Assessment of Wind and Wave Retrieval from Chinese Gaofen-3 SAR Imagery
Sensors 2017, 17(8), 1705; https://doi.org/10.3390/s17081705
Received: 30 May 2017 / Revised: 13 July 2017 / Accepted: 18 July 2017 / Published: 25 July 2017
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Abstract
The Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) launched by the China Academy of Space Technology (CAST) has operated at C-band since September 2016. To date, we have collected 16/42 images in vertical-vertical (VV)/horizontal-horizontal (HH) polarization, covering the National Data Buoy Center (NDBC)
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The Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) launched by the China Academy of Space Technology (CAST) has operated at C-band since September 2016. To date, we have collected 16/42 images in vertical-vertical (VV)/horizontal-horizontal (HH) polarization, covering the National Data Buoy Center (NDBC) buoy measurements of the National Oceanic and Atmospheric Administration (NOAA) around U.S. western coastal waters. Wind speeds from NDBC in situ buoys are up to 15 m/s and buoy-measured significant wave height (SWH) has ranged from 0.5 m to 3 m. In this study, winds were retrieved using the geophysical model function (GMF) together with the polarization ratio (PR) model and waves were retrieved using a new empirical algorithm based on SAR cutoff wavelength in satellite flight direction, herein called CSAR_WAVE. Validation against buoy measurements shows a 1.4/1.9 m/s root mean square error (RMSE) of wind speed and a 24/23% scatter index (SI) of SWH for VV/HH polarization. In addition, wind and wave retrieval results from 166 GF-3 images were compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) re-analysis winds, as well as the SWH from the WaveWatch-III model, respectively. Comparisons show a 2.0 m/s RMSE for wind speed with a 36% SI of SWH for VV-polarization and a 2.2 m/s RMSE for wind speed with a 37% SI of SWH for HH-polarization. Our work gives a preliminary assessment of the wind and wave retrieval results from GF-3 SAR images for the first time and will provide guidance for marine applications of GF-3 SAR. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode
Sensors 2017, 17(7), 1578; https://doi.org/10.3390/s17071578
Received: 1 May 2017 / Revised: 27 June 2017 / Accepted: 28 June 2017 / Published: 5 July 2017
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Abstract
This study aims to detect vessels with lengths ranging from about 70 to 300 m, in Gaofen-3 (GF-3) SAR images with ultrafine strip-map (UFS) mode as fast as possible. Based on the analysis of the characteristics of vessels in GF-3 SAR imagery, an
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This study aims to detect vessels with lengths ranging from about 70 to 300 m, in Gaofen-3 (GF-3) SAR images with ultrafine strip-map (UFS) mode as fast as possible. Based on the analysis of the characteristics of vessels in GF-3 SAR imagery, an effective vessel detection method is proposed in this paper. Firstly, the iterative constant false alarm rate (CFAR) method is employed to detect the potential ship pixels. Secondly, the mean-shift operation is applied on each potential ship pixel to identify the candidate target region. During the mean-shift process, we maintain a selection matrix recording which pixels can be taken, and these pixels are called as the valid points of the candidate target. The l 1 norm regression is used to extract the principal axis and detect the valid points. Finally, two kinds of false alarms, the bright line and the azimuth ambiguity, are removed by comparing the valid area of the candidate target with a pre-defined value and computing the displacement between the true target and the corresponding replicas respectively. Experimental results on three GF-3 SAR images with UFS mode demonstrate the effectiveness and efficiency of the proposed method. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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