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Advances in SAR Image Processing and Applications
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Advances in SAR Image Processing and Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 59555

Special Issue Editors

Dr. Jean-Christophe Cexus

E-Mail Website
Guest Editor
Lab-STICC, UMR CNRS 6285, ENSTA Bretagne, 29806 Brest, France
Interests: signal and image processing; machine learning; computer science; engineering; remote sensing; radar
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ali Khenchaf

E-Mail Website
Guest Editor
Lab-STICC, UMR CNRS 6285, ENSTA Bretagne, 29806 Brest, France
Interests: computer science; engineering; observation; propagation; wave scattering; scattering in random media; monostatic and bistatic scattering; electromagnetic radar cross section; sea clutter; active and passive sensors (Radar, Lidar, Optics, GNSS); radar applications; data assimilation (n-D); sea surface and environment; extraction of parameters from the observed scene: imagery and target parameter estimation; direct and inverse problems; remote sensing of the ocean and the environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, an unprecedented amount of data have been gathered by the radar remote sensing community which are boosting the development of an increasing number of applications for the analysis of our environment.

This is due to the ability of radar sensors to operate independently of solar illuminations and penetrate clouds. In addition to that, interest in these sensors stems from their ability to analyze, detect, localize, and identify the slightest changes in the environment (maritime, terrestrial, urban area, etc.).

These tools have been improved with the synthetic aperture radar (SAR) technique. Thanks to this technique, it is not surprising that radar image processing is commonly used for the study of our Earth (urban planning, environmental sciences, earthquakes, hydrology, littoral zones, oceans, etc.) , and also in the problems of the automatic recognition of targets (ATR) in a heterogeneous environment which is not just terrestrial, but also maritime or aerial to provide, for example, a detailed of the battlefield situational awareness (tanks, howitzers, armored personnel carriers, ships, planes, etc.).

In this Special Issue on SAR Image Processing, we propose an overview of technological and scientific advances in synthetic aperture radar (SAR) image processing and applications. Authors are encouraged to present contributions on the exploitation of satellite or airborne SAR images in various problems. Advanced processing techniques such as Interferometric SAR (InSAR), differential InSAR, or polarimetric SAR imaging are also included.

Therefore, we encourage you to submit your recent and innovative work done in the field of this Special Issue dedicated to “Advances in SAR Image Processing and Applications”.

Dr. Jean-Christophe Cexus
Prof. Dr. Ali Khenchaf
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 submissions that pass pre-check are 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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • Synthetic aperture radar (SAR)
  • Airborne and satellite systems
  • Signal and image processing
  • Machine (deep) learning, compressive sensing
  • Environment monitoring (maritime, terrestrial, urban area, etc.)
  • Applications (remote sensing, target recognition, target detection, target tracking, target location, pollution detection, tomography, etc.)

Published Papers (23 papers)

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Research

20 pages, 10191 KiB  
Article
Road Network Extraction from SAR Images with the Support of Angular Texture Signature and POIs
by Na Sun, Yongjiu Feng, Xiaohua Tong, Zhenkun Lei, Shurui Chen, Chao Wang, Xiong Xu and Yanmin Jin
Remote Sens. 2022, 14(19), 4832; https://doi.org/10.3390/rs14194832 - 28 Sep 2022
Viewed by 1451
Abstract
Urban road network information is an important part of modern spatial information infrastructure and is crucial for high-precision navigation map production and unmanned driving. Synthetic aperture radar (SAR) is a widely used remote-sensing data source, but the complex structure of road networks and [...] Read more.
Urban road network information is an important part of modern spatial information infrastructure and is crucial for high-precision navigation map production and unmanned driving. Synthetic aperture radar (SAR) is a widely used remote-sensing data source, but the complex structure of road networks and the noises in images make it very difficult to extract road information through SAR images. We developed a new method of extracting road network information from SAR images by considering angular (A) and texture (T) features in the sliding windows and points of interest (POIs, or P), and we named this method ATP-ROAD. ATP-ROAD is a sliding window-based semi-automatic approach that uses the grayscale mean, grayscale variance, and binary segmentation information of SAR images as texture features in each sliding window. Since POIs have much-duplicated information, this study also eliminates duplicated POIs considering distance and then selects a combination of POI linkages by discerning the direction of these POIs to initially determine the road direction. The ATP-ROAD method was applied to three experimental areas in Shanghai to extract the road network using China’s Gaofen-3 imagery. The experimental results show that the extracted road network information is relatively complete and matches the actual road conditions, and the result accuracy is high in the three different regions, i.e., 89.57% for Area-I, 96.88% for Area-II, and 92.65% for Area-III. Our method together with our extraction software can be applied to extract information about road networks from SAR images, providing an alternative for enriching the variety of road information. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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20 pages, 3476 KiB  
Article
S2-PCM: Super-Resolution Structural Point Cloud Matching for High-Accuracy Video-SAR Image Registration
by Zhikun Xie, Jun Shi, Yihang Zhou, Xiaqing Yang, Wenxuan Guo and Xiaoling Zhang
Remote Sens. 2022, 14(17), 4302; https://doi.org/10.3390/rs14174302 - 01 Sep 2022
Cited by 4 | Viewed by 1680
Abstract
In this paper, the super-resolution structural point cloud matching (S2-PCM) framework is proposed for video synthetic aperture radar (SAR) inter-frame registration, which consists of a feature recurrence super-resolution network (FRSR-Net), structural point cloud extraction network (SPCE-Net) and robust point matching network [...] Read more.
In this paper, the super-resolution structural point cloud matching (S2-PCM) framework is proposed for video synthetic aperture radar (SAR) inter-frame registration, which consists of a feature recurrence super-resolution network (FRSR-Net), structural point cloud extraction network (SPCE-Net) and robust point matching network (RPM-Net). FRSR-Net is implemented by integrating the feature recurrence structure and residual dense block (RDB) for super-resolution enhancement, SPCE-Net is implemented by training a U-Net with data augmentation, and RPM-Net is applied for robust point cloud matching. Experimental results show that compared with the classical SIFT-like algorithms, S2-PCM achieves higher registration accuracy for video-SAR images under diverse evaluation metrics, such as mutual information (MI), normalized mutual information (NMI), entropy correlation coefficient (ECC), structural similarity (SSIM), etc. The proposed FRSR-Net can significantly improve the quality of video-SAR images and point cloud extraction accuracy. Combining FRSR-Net with S2-PCM, we can obtain higher inter-frame registration accuracy, which is crucial for moving target detection and shadow tracking. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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12 pages, 3562 KiB  
Communication
Feature Selection for SAR Target Discrimination and Efficient Two-Stage Detection Method
by Nam-Hoon Jeong, Jae-Ho Choi, Geon Lee, Ji-Hoon Park and Kyung-Tae Kim
Remote Sens. 2022, 14(16), 4044; https://doi.org/10.3390/rs14164044 - 19 Aug 2022
Cited by 3 | Viewed by 1296
Abstract
Feature-based target detection in synthetic aperture radar (SAR) images is required for monitoring situations where it is difficult to obtain a large amount of data, such as in tactical regions. Although many features have been studied for target detection in SAR images, their [...] Read more.
Feature-based target detection in synthetic aperture radar (SAR) images is required for monitoring situations where it is difficult to obtain a large amount of data, such as in tactical regions. Although many features have been studied for target detection in SAR images, their performance depends on the characteristics of the images, and both efficiency and performance deteriorate when the features are used indiscriminately. In this study, we propose a two-stage detection framework to ensure efficient and superior detection performance in TSX images, using previously studied features. The proposed method consists of two stages. The first stage uses simple features to eliminate misdetections. Next, the discrimination performance for the target and clutter of each feature is evaluated and those features suitable for the image are selected. In addition, the Karhunen–Loève (KL) transform reduces the redundancy of the selected features and maximizes discrimination performance. By applying the proposed method to actual TerraSAR-X (TSX) images, the majority of the identified clusters of false detections were excluded, and the target of interest could be distinguished. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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20 pages, 7645 KiB  
Article
Performance Evaluation of Interest Point Detectors for Heterologous Image Matching
by Zhengbin Wang, Anxi Yu, Zhen Dong, Ben Zhang and Xing Chen
Remote Sens. 2022, 14(15), 3724; https://doi.org/10.3390/rs14153724 - 03 Aug 2022
Cited by 1 | Viewed by 1339
Abstract
In point-based heterologous image matching algorithms, high-quality interest point detection directly affects the final image matching quality. In this paper, starting from the detection mechanism of each interest point detector, optical images and SAR images with different resolutions and covering different areas are [...] Read more.
In point-based heterologous image matching algorithms, high-quality interest point detection directly affects the final image matching quality. In this paper, starting from the detection mechanism of each interest point detector, optical images and SAR images with different resolutions and covering different areas are selected as experimental data. The five state-of-the-art SAR-Harris, UND-Harris, Har-DoG, Harris-Laplace and DoG interest point detectors are analyzed in terms of scale difference adaptability, nonlinear intensity difference adaptability, distribution uniformity, image registration alignment performance and detection efficiency. Then, we performed registration experiments on images from different sensors, at different times, and at different resolutions to further validate our evaluation results. Finally, the applicable image types of each detector are summarized. The experimental results show that SAR-Harris has the best performance in scale difference adaptability, and UND-Harris has the weakest performance. In terms of nonlinear intensity difference adaptability, SAR-Harris and UND-Harris are comparable, and DoG performance is the weakest. The distribution uniformity of UND-Harris is significantly better than other detectors. Although Har-DoG is weaker than Har-Lap and DoG in repeatability, it is better than both in final image alignment performance. DoG is superior in detection efficiency, followed by SAR-Harris. A comprehensive evaluation and a large amount of experimental data are used to evaluate and summarize each detector in detail. This paper provides a useful guide for the selection of interest point detectors during heterologous image matching. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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20 pages, 5523 KiB  
Article
A Sidelobe Suppression Method for Circular Ground-Based SAR 3D Imaging Based on Sparse Optimization of Radial Phase-Center Distribution
by Qiming Zhang, Jinping Sun, Yanping Wang and Yun Lin
Remote Sens. 2022, 14(14), 3248; https://doi.org/10.3390/rs14143248 - 06 Jul 2022
Cited by 1 | Viewed by 1149
Abstract
Circular ground-based SAR (GBSAR) is a new 3D imaging GBSAR with the potential of acquiring high-quality 3D SAR images and 3D deformation. However, its donut-shaped spectrum and short radius of antenna rotation cause high sidelobes on 3D curved surfaces, resulting in 3D SAR [...] Read more.
Circular ground-based SAR (GBSAR) is a new 3D imaging GBSAR with the potential of acquiring high-quality 3D SAR images and 3D deformation. However, its donut-shaped spectrum and short radius of antenna rotation cause high sidelobes on 3D curved surfaces, resulting in 3D SAR images with poor quality. The multi-phase-center circular GBSAR with full array can effectively suppress sidelobes by filling the donut-shaped spectrum to be the equivalent solid spectrum, but it requires a larger number of phase centers, increasing system cost and engineering difficulties. In this paper, a sidelobe suppression method for circular GBSAR 3D imaging based on sparse optimization of radial phase-center distribution is proposed to suppress high sidelobes at low cost. By deriving the point spread function (PSF) of multi-phase-center circular GBSAR and taking the peak sidelobe level (PSL) and integrated sidelobe level (ISL) of the derived PSF as multi-objective functions, we solve the multi-objective optimization problem to optimize the sparse distribution of radial phase centers. The advantage of the proposed method is that the solved optimal radial phase-center distribution can effectively suppress the 3D sidelobes of circular GBSAR with a limited number of phase centers. Finally, the sidelobe suppression effect of the proposed method is verified via 3D imaging simulations. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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23 pages, 11531 KiB  
Article
Ship Detection in SAR Images Based on Feature Enhancement Swin Transformer and Adjacent Feature Fusion
by Kuoyang Li, Min Zhang, Maiping Xu, Rui Tang, Liang Wang and Hai Wang
Remote Sens. 2022, 14(13), 3186; https://doi.org/10.3390/rs14133186 - 02 Jul 2022
Cited by 18 | Viewed by 3095
Abstract
Convolutional neural networks (CNNs) have achieved milestones in object detection of synthetic aperture radar (SAR) images. Recently, vision transformers and their variants have shown great promise in detection tasks. However, ship detection in SAR images remains a substantial challenge because of the characteristics [...] Read more.
Convolutional neural networks (CNNs) have achieved milestones in object detection of synthetic aperture radar (SAR) images. Recently, vision transformers and their variants have shown great promise in detection tasks. However, ship detection in SAR images remains a substantial challenge because of the characteristics of strong scattering, multi-scale, and complex backgrounds of ship objects in SAR images. This paper proposes an enhancement Swin transformer detection network, named ESTDNet, to complete the ship detection in SAR images to solve the above problems. We adopt the Swin transformer of Cascade-R-CNN (Cascade R-CNN Swin) as a benchmark model in ESTDNet. Based on this, we built two modules in ESTDNet: the feature enhancement Swin transformer (FESwin) module for improving feature extraction capability and the adjacent feature fusion (AFF) module for optimizing feature pyramids. Firstly, the FESwin module is employed as the backbone network, aggregating contextual information about perceptions before and after the Swin transformer model using CNN. It uses single-point channel information interaction as the primary and local spatial information interaction as the secondary for scale fusion based on capturing visual dependence through self-attention, which improves spatial-to-channel feature expression and increases the utilization of ship information from SAR images. Secondly, the AFF module is a weighted selection fusion of each high-level feature in the feature pyramid with its adjacent shallow-level features using learnable adaptive weights, allowing the ship information of SAR images to be focused on the feature maps at more scales and improving the recognition and localization capability for ships in SAR images. Finally, the ablation study conducted on the SSDD dataset validates the effectiveness of the two components proposed in the ESTDNet detector. Moreover, the experiments executed on two public datasets consisting of SSDD and SARShip demonstrate that the ESTDNet detector outperforms the state-of-the-art methods, which provides a new idea for ship detection in SAR images. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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21 pages, 10306 KiB  
Article
An Improved L2Net for Repetitive Texture Image Registration with Intensity Difference Heterogeneous SAR Images
by Peng Men, Hao Guo, Jubai An and Guanyu Li
Remote Sens. 2022, 14(11), 2527; https://doi.org/10.3390/rs14112527 - 25 May 2022
Cited by 3 | Viewed by 1628
Abstract
Heterogeneous synthetic aperture radar (SAR) images contain more complementary information compared with homologous SAR images; thus, the comprehensive utilization of heterogeneous SAR images could potentially improve performance for the monitoring of sea surface objects, such as sea ice and enteromorpha. Image registration is [...] Read more.
Heterogeneous synthetic aperture radar (SAR) images contain more complementary information compared with homologous SAR images; thus, the comprehensive utilization of heterogeneous SAR images could potentially improve performance for the monitoring of sea surface objects, such as sea ice and enteromorpha. Image registration is key to the application of monitoring sea surface objects. Heterogeneous SAR images have intensity differences and resolution differences, and after the uniform resolution, intensity differences are one of the most important factors affecting the image registration accuracy. In addition, sea surface objects have numerous repetitive and confusing features for feature extraction, which also limits the image registration accuracy. In this paper, we propose an improved L2Net network for image registration with intensity differences and repetitive texture features, using sea ice as the research object. The deep learning network can capture feature correlations between image patch pairs, and can obtain the correct matching from a large number of features with repetitive texture. In the SAR image pair, four patches of different sizes centered on the corner points are proposed as inputs. Thus, local features and more global features are fused to obtain excellent structural features, to distinguish between different repetitive textural features, add contextual information, further improve the feature correlation, and improve the accuracy of image registration. An outlier removal strategy is proposed to remove false matches due to repetitive textures. Finally, the effectiveness of our method was verified by comparative experiments. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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19 pages, 30719 KiB  
Article
Adaptive Max-Margin One-Class Classifier for SAR Target Discrimination in Complex Scenes
by Leiyao Liao, Lan Du, Wei Zhang and Jian Chen
Remote Sens. 2022, 14(9), 2078; https://doi.org/10.3390/rs14092078 - 26 Apr 2022
Viewed by 1229
Abstract
Synthetic aperture radar (SAR) target discrimination is an important stage that distinguishes targets from clutters in the radar automatic target recognition field. However, in complex SAR scenes, the performance of some traditional discriminators will degrade. As an effective tool for one-class classification (OCC), [...] Read more.
Synthetic aperture radar (SAR) target discrimination is an important stage that distinguishes targets from clutters in the radar automatic target recognition field. However, in complex SAR scenes, the performance of some traditional discriminators will degrade. As an effective tool for one-class classification (OCC), the max-margin one-class classifier has attracted much attention for SAR target discrimination, as it can effectively reduce the impact of multiple clutters. However, the performance of the max-margin one-class classifier is very sensitive to the values of kernel parameters. To solve the problem, this paper proposes an adaptive max-margin one-class classifier for SAR target discrimination in complex scenes. In a max-margin one-class classifier with a suitable kernel parameter, the distance between a sample and classification boundary satisfies a certain geometric relationship, i.e., edge samples in input space are transformed to the region in the kernel space close to boundary, while interior samples in input space are transformed to the region in the kernel space far away from boundary. Therefore, we define the information entropy of samples in the kernel space to measure the distance between samples and classification boundary. To automatically obtain the optimal kernel parameter of the max-margin one-class classifier, the edge and interior samples in the input space are first selected, and then the parameter optimization is performed by minimizing information entropy of interior samples and simultaneously maximizing the information entropy of edge samples. Experimental results of the synthetic datasets and measured synthetic aperture radar (SAR) datasets validate the effectiveness of our method. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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24 pages, 5292 KiB  
Article
Space-Based Detection of Significant Water-Depth Increase Induced by Hurricane Irma in the Everglades Wetlands Using Sentinel-1 SAR Backscatter Observations
by Boya Zhang, Shimon Wdowinski and Daniel Gann
Remote Sens. 2022, 14(6), 1415; https://doi.org/10.3390/rs14061415 - 15 Mar 2022
Cited by 4 | Viewed by 2295
Abstract
Extreme rainfall, induced by severe weather events, such as hurricanes, impacts wetlands because rapid water-depth increases can lead to flora and fauna mortality. This study developed an innovative algorithm to detect significant water-depth increases (SWDI, defined as water-depth increases above a threshold) in [...] Read more.
Extreme rainfall, induced by severe weather events, such as hurricanes, impacts wetlands because rapid water-depth increases can lead to flora and fauna mortality. This study developed an innovative algorithm to detect significant water-depth increases (SWDI, defined as water-depth increases above a threshold) in wetlands, using Sentinel-1 SAR backscatter. We used Hurricane Irma as an example that made landfall in the south Florida Everglades wetlands in September 2017 and produced tremendous rainfall. The algorithm detects SWDI for during- and post-event SAR acquisition dates, using pre-event water-depth as a baseline. The algorithm calculates Normalized Difference Backscatter Index (NDBI), using pre-, during-, and post-event backscatter, at a 20-m SAR resolution, as an indicator of the likelihood of SWDI, and detects SWDI using all NDBI values in a 400-m resolution pixel. The algorithm successfully detected large SWDI areas for the during-event date and progressive expansion of non-SWDI areas (water-depth differences less than the threshold) for five post-event dates in the following two months. The algorithm achieved good performance in both ‘herbaceous dominant’ and ‘trees embedded within herbaceous matrix’ land covers, with an overall accuracy of 81%. This study provides a solution for accurate mapping of SWDI and can be used in global wetlands, vulnerable to extreme rainfall. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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13 pages, 4226 KiB  
Communication
Removal of Ionospheric Effects from Sigma Naught Images of the ALOS/PALSAR-2 Satellite
by Fábio Furlan Gama, Natalia Cristina Wiederkehr and Polyanna da Conceição Bispo
Remote Sens. 2022, 14(4), 962; https://doi.org/10.3390/rs14040962 - 16 Feb 2022
Cited by 3 | Viewed by 1830
Abstract
The monitoring of forest degradation in the Amazon through radar remote sensing methodologies has increased intensely in recent years. Synthetic aperture radar (SAR) sensors that operate in L-band have an interesting response for land use and land cover (LULC) as well as for [...] Read more.
The monitoring of forest degradation in the Amazon through radar remote sensing methodologies has increased intensely in recent years. Synthetic aperture radar (SAR) sensors that operate in L-band have an interesting response for land use and land cover (LULC) as well as for aboveground biomass (AGB). Depending on the magnetic and solar activities and seasonality, plasma bubbles in the ionosphere appear in the equatorial and tropical regions; these factors can cause stripes across SAR images, which disturb the interpretation and the classification. Our article shows a methodology to filter these stripes using Fourier fast transform (FFT), in which a stop-band filter removes this noise. In order to make this possible, we used Environment for Visualizing Images (ENVI), Sentinel Application Platform (SNAP), and Interactive Data Language (IDL). The final filtered scenes were classified by random forest (RF), and the results of this classification showed superior performance compared to the original scenes, showing this methodology can help to recover historic series of L-band images. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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19 pages, 3519 KiB  
Article
SAR Image Despeckling Based on Block-Matching and Noise-Referenced Deep Learning Method
by Chen Wang, Zhixiang Yin, Xiaoshuang Ma and Zhutao Yang
Remote Sens. 2022, 14(4), 931; https://doi.org/10.3390/rs14040931 - 14 Feb 2022
Cited by 6 | Viewed by 2289
Abstract
The noise2noise-based despeckling method, capable of training the despeckling deep neural network with only noisy synthetic aperture radar (SAR) image, has presented very good performance in recent research. This method requires a fine-registered multi-temporal dataset with minor time variance and uses similarity estimation [...] Read more.
The noise2noise-based despeckling method, capable of training the despeckling deep neural network with only noisy synthetic aperture radar (SAR) image, has presented very good performance in recent research. This method requires a fine-registered multi-temporal dataset with minor time variance and uses similarity estimation to compensate for the time variance. However, constructing such a training dataset is very time-consuming and may not be viable for a certain practitioner. In this article, we propose a novel single-image-capable speckling method that combines the similarity-based block-matching and noise referenced deep learning network. The denoising network designed for this method is an encoder–decoder convolutional neural network and is accommodated to small image patches. This method firstly constructs a large number of noisy pairs as training input by similarity-based block-matching in either one noisy SAR image or multiple images. Then, the method trains the network in a Siamese manner with two parameter-sharing branches. The proposed method demonstrates favorable despeckling performance with both simulated and real SAR data with respect to other state-of-the-art reference filters. It also presents satisfying generalization capability as the trained network can despeckle well the unseen image of the same sensor. The main advantage of the proposed method is its application flexibility. It could be trained with either one noisy image or multiple images. Furthermore, the despeckling could be inferred by either the ad hoc trained network or a pre-trained one of the same sensor. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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23 pages, 27750 KiB  
Article
Space-Based Displacement Monitoring of Coastal Urban Areas: The Case of Limassol’s Coastal Front
by Kyriaki Fotiou, Dimitris Kakoullis, Marina Pekri, George Melillos, Ramon Brcic, Michael Eineder, Diofantos G. Hadjimitsis and Chris Danezis
Remote Sens. 2022, 14(4), 914; https://doi.org/10.3390/rs14040914 - 14 Feb 2022
Cited by 3 | Viewed by 2296
Abstract
In the last five years, the urban development of the city of Limassol has rapidly increased in the sectors of industry, trade, real estate, and many others. This exponentially increased urban development arises several concerns about the aggravation of potential land subsidence in [...] Read more.
In the last five years, the urban development of the city of Limassol has rapidly increased in the sectors of industry, trade, real estate, and many others. This exponentially increased urban development arises several concerns about the aggravation of potential land subsidence in the Limassol coastal front. Forty six Copernicus Sentinel-1 acquisitions from 2017 to 2021 have been processed and analyzed using the Sentinel Application Platform (SNAP) and the Stanford Method for Persistent Scatterers (StaMPS). A case study for the identification and analysis of the persistent scatterers (PS) in pixels in a series of interferograms and the quantity of the land displacements in the line of sight of the Limassol coastal front is presented in this research, with subsidence rates up to about (−5 to 4 mm/year). For the validation of the detected deformation, accurate ground-based geodetic measurements along the coastal area were used. Concordantly, considering that there is a significant number of skyscrapers planned or currently under construction, this study attempts a preliminary assessment of the impact these structures will pose on the coastal front of the area of Limassol. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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22 pages, 8985 KiB  
Article
A Novel Speckle Suppression Method with Quantitative Combination of Total Variation and Anisotropic Diffusion PDE Model
by Jiamu Li, Zijian Wang, Wenbo Yu, Yunhua Luo and Zhongjun Yu
Remote Sens. 2022, 14(3), 796; https://doi.org/10.3390/rs14030796 - 08 Feb 2022
Cited by 7 | Viewed by 1797
Abstract
Speckle noise seriously affects synthetic aperture radar (SAR) image application. Speckle suppression aims to smooth the homogenous region while preserving edge and texture in the image. A novel speckle suppression method based on the combination of total variation and partial differential equation denoising [...] Read more.
Speckle noise seriously affects synthetic aperture radar (SAR) image application. Speckle suppression aims to smooth the homogenous region while preserving edge and texture in the image. A novel speckle suppression method based on the combination of total variation and partial differential equation denoising models is proposed in this paper. Taking full account of the local statistics in the image, a quantization technique—which is different from the normal edge detection method—is supported by the variation coefficient of the image. Accordingly, a quantizer is designed to respond to both noise level and edge strength. This quantizer automatically determines the threshold of diffusion coefficient and controls the weight between total variation filter and anisotropic diffusion partial differential equation filter. A series of experiments are conducted to test the performance of the quantizer and proposed filter. Extensive experimental results have demonstrated the superiority of the proposed method with both synthetic images and natural SAR images. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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30 pages, 16183 KiB  
Article
Radio Frequency Interference Mitigation for Synthetic Aperture Radar Based on the Time-Frequency Constraint Joint Low-Rank and Sparsity Properties
by Yi Ding, Weiwei Fan, Zijing Zhang, Feng Zhou and Bingbing Lu
Remote Sens. 2022, 14(3), 775; https://doi.org/10.3390/rs14030775 - 07 Feb 2022
Cited by 8 | Viewed by 2214
Abstract
Synthetic aperture radar (SAR) is susceptible to radio frequency interference (RFI), which becomes especially commonplace in the increasingly complex electromagnetic environments. RFI severely detracts from SAR imaging quality, which hinders image interpretation. Therefore, some RFI mitigation algorithms have been introduced based on the [...] Read more.
Synthetic aperture radar (SAR) is susceptible to radio frequency interference (RFI), which becomes especially commonplace in the increasingly complex electromagnetic environments. RFI severely detracts from SAR imaging quality, which hinders image interpretation. Therefore, some RFI mitigation algorithms have been introduced based on the partial features of RFI, but the RFI reconstruction models in these algorithms are rough and can be improved further. This paper proposes two algorithms for accurately modeling the structural properties of RFI and target echo signal (TES). Firstly, an RFI mitigation algorithm joining the low-rank characteristic and dual-sparsity property (LRDS) is proposed. In this algorithm, RFI is treated as a low-rank and sparse matrix, and the sparse matrix assumption is made for TES in the time–frequency (TF) domain. Compared with the traditional low-rank and sparse models, it can achieve better RFI mitigation performance with less signal loss and accelerated algorithm convergence. Secondly, the other RFI mitigation algorithm, named as TFC-LRS, is proposed to further reduce the signal loss. The TF constraint concept, in lieu of the special sparsity, is introduced in this algorithm to describe the structural distribution of RFI because of its aggregation characteristic in the TF spectrogram. Finally, the effectiveness, superiority, and robustness of the proposed algorithms are verified by RFI mitigation experiments on the simulated and measured SAR datasets. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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22 pages, 10663 KiB  
Article
Semi-Supervised SAR Target Detection Based on an Improved Faster R-CNN
by Leiyao Liao, Lan Du and Yuchen Guo
Remote Sens. 2022, 14(1), 143; https://doi.org/10.3390/rs14010143 - 29 Dec 2021
Cited by 58 | Viewed by 8911
Abstract
In the remote sensing image processing field, the synthetic aperture radar (SAR) target-detection methods based on convolutional neural networks (CNNs) have gained remarkable performance relying on large-scale labeled data. However, it is hard to obtain many labeled SAR images. Semi-supervised learning is an [...] Read more.
In the remote sensing image processing field, the synthetic aperture radar (SAR) target-detection methods based on convolutional neural networks (CNNs) have gained remarkable performance relying on large-scale labeled data. However, it is hard to obtain many labeled SAR images. Semi-supervised learning is an effective way to address the issue of limited labels on SAR images because it uses unlabeled data. In this paper, we propose an improved faster regions with CNN features (R-CNN) method, with a decoding module and a domain-adaptation module called FDDA, for semi-supervised SAR target detection. In FDDA, the decoding module is adopted to reconstruct all the labeled and unlabeled samples. In this way, a large number of unlabeled SAR images can be utilized to help structure the latent space and learn the representative features of the SAR images, devoting attention to performance promotion. Moreover, the domain-adaptation module is further introduced to utilize the unlabeled SAR images to promote the discriminability of features with the assistance of the abundantly labeled optical remote sensing (ORS) images. Specifically, the transferable features between the ORS images and SAR images are learned to reduce the domain discrepancy via the mean embedding matching, and the knowledge of ORS images is transferred to the SAR images for target detection. Ultimately, the joint optimization of the detection loss, reconstruction, and domain adaptation constraints leads to the promising performance of the FDDA. The experimental results on the measured SAR image datasets and the ORS images dataset indicate that our method achieves superior SAR target detection performance with limited labeled SAR images. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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22 pages, 6147 KiB  
Article
A Comparative Study of Active Rock Glaciers Mapped from Geomorphic- and Kinematic-Based Approaches in Daxue Shan, Southeast Tibetan Plateau
by Jiaxin Cai, Xiaowen Wang, Guoxiang Liu and Bing Yu
Remote Sens. 2021, 13(23), 4931; https://doi.org/10.3390/rs13234931 - 04 Dec 2021
Cited by 5 | Viewed by 2995
Abstract
Active rock glaciers (ARGs) are important permafrost landforms in alpine regions. Identifying ARGs has mainly relied on visual interpretation of their geomorphic characteristics with optical remote sensing images, while mapping ARGs from their kinematic features has also become popular in recent years. However, [...] Read more.
Active rock glaciers (ARGs) are important permafrost landforms in alpine regions. Identifying ARGs has mainly relied on visual interpretation of their geomorphic characteristics with optical remote sensing images, while mapping ARGs from their kinematic features has also become popular in recent years. However, a thorough comparison of geomorphic- and kinematic-based inventories of ARGs has not been carried out. In this study, we employed a multi-temporal interferometric synthetic aperture radar (InSAR) technique to derive the mean annual surface displacement velocity over the Daxue Shan, Southeast Tibet Plateau. We then compiled a rock glacier inventory by synergistically interpreting the InSAR-derived surface displacements and geomorphic features based on Google Earth images. Our InSAR-assist kinematic-based inventory (KBI) was further compared with a pre-existing geomorphic-based inventory (GBI) of rock glaciers in Daxue Shan. The results show that our InSAR-assist inventory consists of 344 ARGs, 36% (i.e., 125) more than that derived from the geomorphic-based method (i.e., 251). Only 32 ARGs in the GBI are not included in the KBI. Among the 219 ARGs detected by both approaches, the ones with area differences of more than 20% account for about 32% (i.e., 70 ARGs). The mean downslope velocities of ARGs calculated from InSAR are between 2.8 and 107.4 mm∙a−1. Our comparative analyses show that ARGs mapping from the InSAR-based kinematic approach is more efficient and accurate than the geomorphic-based approach. Nonetheless, the completeness of the InSAR-assist KBI is affected by the SAR data acquisition time, signal decorrelation, geometric distortion of SAR images, and the sensitivity of the InSAR measurement to ground deformation. We suggest that the kinematic-based approach should be utilized in future ARGs-based studies such as regional permafrost distribution assessment and water storage estimates. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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21 pages, 11872 KiB  
Article
A Novel Method for Layover Detection in Mountainous Areas with SAR Images
by Lin Wu, Hongxia Wang, Yuan Li, Zhengwei Guo and Ning Li
Remote Sens. 2021, 13(23), 4882; https://doi.org/10.3390/rs13234882 - 01 Dec 2021
Cited by 7 | Viewed by 2195
Abstract
It is well known that there are geometric distortions in synthetic aperture radar (SAR) images when the terrain undulates. Layover is the most common one, which brings challenges to the application of SAR remote sensing. This study proposes a novel detection method that [...] Read more.
It is well known that there are geometric distortions in synthetic aperture radar (SAR) images when the terrain undulates. Layover is the most common one, which brings challenges to the application of SAR remote sensing. This study proposes a novel detection method that is mainly aimed at the layover caused by mountains and can be performed with only medium-resolution SAR images and no other auxiliary data. The detection includes the following four stages: initial processing, difference image calculation and rough and fine layover detection. Initial processing mainly obtains the potential layover areas, which are mixed with the built-up areas after classification. Additionally, according to the analysis of the backscatter coefficient (BC) of various ground objects with different polarization images, the layover areas are detected step-by-step from the mixed areas, in which the region-based FCM segmentation algorithm and spatial relationship criteria are used. Taking the Danjiangkou Reservoir area as the study area, the relevant experiments with Sentinel-1A SAR images were conducted. The quantitative analysis of detection results adopted the figure of merit (FoM), and the highest accuracy was up to 87.6% of one selected validation region. Experiments in the South Taihang area also showed the satisfactory effect of layover detection, and the values of FoM were all above 85%. These results show that the proposed method can do well in the layover detection caused by mountains. Its simplicity and effectiveness are helpful in removing the influence of layover on SAR image applications to a certain extent and improving the development of SAR remote sensing technology. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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16 pages, 71531 KiB  
Article
Mapping and Characterizing Displacements of Landslides with InSAR and Airborne LiDAR Technologies: A Case Study of Danba County, Southwest China
by Qiang Xu, Chen Guo, Xiujun Dong, Weile Li, Huiyan Lu, Hao Fu and Xiaosha Liu
Remote Sens. 2021, 13(21), 4234; https://doi.org/10.3390/rs13214234 - 21 Oct 2021
Cited by 25 | Viewed by 3486
Abstract
Interferometric synthetic aperture radar (InSAR) technology is known as one of the most effective methods for active landslide identification and deformation monitoring in large areas, and thus it is conducive to preventing and mitigating the losses caused by landslides. However, great uncertainty inevitably [...] Read more.
Interferometric synthetic aperture radar (InSAR) technology is known as one of the most effective methods for active landslide identification and deformation monitoring in large areas, and thus it is conducive to preventing and mitigating the losses caused by landslides. However, great uncertainty inevitably exists due to influences of complex terrains, dense vegetations, and atmospheric interferences in the southwestern mountainous area of China, and this is associated with false or erroneous judgment during the process of landslide identification. In this study, a landslide identification method is put forward by integrating InSAR technology and airborne light detection and ranging (LiDAR) technology. Via this method, surface deformation characteristics detected by InSAR technology and micro-geomorphic features reflected by LiDAR technology were used to identify and map landslides of large areas. Herein, the method was applied to process 224 Sentinel-1 images covering Danba County and its surrounding areas (540 km2) from October 2014 to September 2020. Firstly, 44 active landslides with total areas of 59 km2 were detected by stacking InSAR technology. Then, major regions up to 135 km2 were validated by data gained from the airborne LiDAR technology. Particularly, several large landslides with lengths and/or widths of more than 2 km were found. Further, the precipitation data were integrated with the above results to analyze the temporal deformation characteristics of three typical landslides from major regions via SBAS InSAR technology. The key findings were as follows: (1) The combination of InSAR and LiDAR technologies could improve the accuracy of landslide detection and identification; (2) there was a significant correlation between temporal deformation characteristics of some landslides and monthly rainfall, with an obvious hysteretic effect existing between the initiation timing of rainfall and that of deformation; (3) the results of this study will be important guidance for the prevention and control of geological hazards in Danba County and areas with similar complex geomorphological conditions by helping effectively identify and map landslides. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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23 pages, 4217 KiB  
Article
Two-Stream Deep Fusion Network Based on VAE and CNN for Synthetic Aperture Radar Target Recognition
by Lan Du, Lu Li, Yuchen Guo, Yan Wang, Ke Ren and Jian Chen
Remote Sens. 2021, 13(20), 4021; https://doi.org/10.3390/rs13204021 - 09 Oct 2021
Cited by 17 | Viewed by 2649
Abstract
Usually radar target recognition methods only use a single type of high-resolution radar signal, e.g., high-resolution range profile (HRRP) or synthetic aperture radar (SAR) images. In fact, in the SAR imaging procedure, we can simultaneously obtain both the HRRP data and the corresponding [...] Read more.
Usually radar target recognition methods only use a single type of high-resolution radar signal, e.g., high-resolution range profile (HRRP) or synthetic aperture radar (SAR) images. In fact, in the SAR imaging procedure, we can simultaneously obtain both the HRRP data and the corresponding SAR image, as the information contained within them is not exactly the same. Although the information contained in the HRRP data and the SAR image are not exactly the same, both are important for radar target recognition. Therefore, in this paper, we propose a novel end-to-end two stream fusion network to make full use of the different characteristics obtained from modeling HRRP data and SAR images, respectively, for SAR target recognition. The proposed fusion network contains two separated streams in the feature extraction stage, one of which takes advantage of a variational auto-encoder (VAE) network to acquire the latent probabilistic distribution characteristic from the HRRP data, and the other uses a lightweight convolutional neural network, LightNet, to extract the 2D visual structure characteristics based on SAR images. Following the feature extraction stage, a fusion module is utilized to integrate the latent probabilistic distribution characteristic and the structure characteristic for the reflecting target information more comprehensively and sufficiently. The main contribution of the proposed method consists of two parts: (1) different characteristics from the HRRP data and the SAR image can be used effectively for SAR target recognition, and (2) an attention weight vector is used in the fusion module to adaptively integrate the different characteristics from the two sub-networks. The experimental results of our method on the HRRP data and SAR images of the MSTAR and civilian vehicle datasets obtained improvements of at least 0.96 and 2.16%, respectively, on recognition rates, compared with current SAR target recognition methods. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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21 pages, 4846 KiB  
Article
Drone SAR Image Compression Based on Block Adaptive Compressive Sensing
by Jihoon Choi and Wookyung Lee
Remote Sens. 2021, 13(19), 3947; https://doi.org/10.3390/rs13193947 - 02 Oct 2021
Cited by 2 | Viewed by 1893
Abstract
In this paper, an adaptive block compressive sensing (BCS) method is proposed for compression of synthetic aperture radar (SAR) images. The proposed method enhances the compression efficiency by dividing the magnitude of the entire SAR image into multiple blocks and subsampling individual blocks [...] Read more.
In this paper, an adaptive block compressive sensing (BCS) method is proposed for compression of synthetic aperture radar (SAR) images. The proposed method enhances the compression efficiency by dividing the magnitude of the entire SAR image into multiple blocks and subsampling individual blocks with different compression ratios depending on the sparsity of coefficients in the discrete wavelet transform domain. Especially, a new algorithm is devised that selects the best block measurement matrix from a predetermined codebook to reduce the side information about measurement matrices transferred from the remote sensing node to the ground station. Through some modification of the iterative thresholding algorithm, a new clustered BCS recovery method is proposed that classifies the blocks into multiple clusters according to the compression ratio and iteratively reconstructs the SAR image from the received compressed data. Since the blocks in the same cluster are concurrently reconstructed using the same measurement matrix, the proposed structure mitigates the increase in computational complexity when adopting multiple measurement matrices. Using existing SAR images and experimental data obtained by self-made drone SAR and vehicular SAR systems, it is shown that the proposed scheme provides a good tradeoff between the peak signal-to-noise ratio and the computational load compared to conventional BCS-based compression techniques. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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20 pages, 6533 KiB  
Article
A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
by Jianghao Tian, Yulun Wu, Yonghua Cai, Huaitao Fan and Weidong Yu
Remote Sens. 2021, 13(15), 2866; https://doi.org/10.3390/rs13152866 - 22 Jul 2021
Cited by 11 | Viewed by 2200
Abstract
Accurate and efficient image mosaicking is essential for generating wide-range swath images of spaceborne scanning synthetic aperture radar (ScanSAR). However, the existing methods cannot guarantee the accuracy and efficiency of stitching simultaneously, especially when mosaicking multiple large-area images. In this paper, we propose [...] Read more.
Accurate and efficient image mosaicking is essential for generating wide-range swath images of spaceborne scanning synthetic aperture radar (ScanSAR). However, the existing methods cannot guarantee the accuracy and efficiency of stitching simultaneously, especially when mosaicking multiple large-area images. In this paper, we propose a novel image mosaic method based on homography matrix compensation to solve the mentioned problem. A set of spaceborne ScanSAR images from the Gaofen-3 (GF-3) satellite were selected to test the performance of the new method. First, images are preprocessed by an improved Wallis filter to eliminate intensity inconsistencies. Then, to reduce the enormous computational redundancy of registration, the overlapping areas of adjacent images are coarsely extracted using geolocation technologies. Furthermore, to improve the efficiency of stitching and maintain the original information and resolution of images, we deduce a compensation of homography matrix to implement downsampled images registration and original-size images projection. After stitching, the transitions at the edges of the images were smooth and seamless, the information and resolution of the original images were preserved successfully, and the efficiency of the mosaic was improved by approximately one thousand-fold. The validity, high efficiency and reliability of the method are verified. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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21 pages, 5721 KiB  
Article
Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism
by Di Wei, Yuang Du, Lan Du and Lu Li
Remote Sens. 2021, 13(14), 2686; https://doi.org/10.3390/rs13142686 - 08 Jul 2021
Cited by 18 | Viewed by 2954
Abstract
The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train the network. Moreover, some clutter is very similar to [...] Read more.
The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train the network. Moreover, some clutter is very similar to targets in SAR images with complex scenes, making the target detection task very difficult. Therefore, a SAR target detection network based on a semi-supervised learning and attention mechanism is proposed in this paper. Since the image-level label simply marks whether the image contains the target of interest or not, which is easier to be labeled than the target-level label, the proposed method uses a small number of target-level labeled training samples and a large number of image-level labeled training samples to train the network with a semi-supervised learning algorithm. The proposed network consists of a detection branch and a scene recognition branch with a feature extraction module and an attention module shared between these two branches. The feature extraction module can extract the deep features of the input SAR images, and the attention module can guide the network to focus on the target of interest while suppressing the clutter. During the semi-supervised learning process, the target-level labeled training samples will pass through the detection branch, while the image-level labeled training samples will pass through the scene recognition branch. During the test process, considering the help of global scene information in SAR images for detection, a novel coarse-to-fine detection procedure is proposed. After the coarse scene recognition determining whether the input SAR image contains the target of interest or not, the fine target detection is performed on the image that may contain the target. The experimental results based on the measured SAR dataset demonstrate that the proposed method can achieve better performance than the existing methods. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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21 pages, 3716 KiB  
Article
Fast SAR Autofocus Based on Ensemble Convolutional Extreme Learning Machine
by Zhi Liu, Shuyuan Yang, Zhixi Feng, Quanwei Gao and Min Wang
Remote Sens. 2021, 13(14), 2683; https://doi.org/10.3390/rs13142683 - 07 Jul 2021
Cited by 17 | Viewed by 2778
Abstract
Inaccurate Synthetic Aperture Radar (SAR) navigation information will lead to unknown phase errors in SAR data. Uncompensated phase errors can blur the SAR images. Autofocus is a technique that can automatically estimate phase errors from data. However, existing autofocus algorithms either have poor [...] Read more.
Inaccurate Synthetic Aperture Radar (SAR) navigation information will lead to unknown phase errors in SAR data. Uncompensated phase errors can blur the SAR images. Autofocus is a technique that can automatically estimate phase errors from data. However, existing autofocus algorithms either have poor focusing quality or a slow focusing speed. In this paper, an ensemble learning-based autofocus method is proposed. Convolutional Extreme Learning Machine (CELM) is constructed and utilized to estimate the phase error. However, the performance of a single CELM is poor. To overcome this, a novel, metric-based combination strategy is proposed, combining multiple CELMs to further improve the estimation accuracy. The proposed model is trained with the classical bagging-based ensemble learning method. The training and testing process is non-iterative and fast. Experimental results conducted on real SAR data show that the proposed method has a good trade-off between focusing quality and speed. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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