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Polarimetric SAR Techniques and Applications

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 April 2017) | Viewed by 60396

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Dr. Carlos López-Martínez

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Signal Theory and Communications Department, Universitat Politècnica de Catalunya—Bardelona Tech. (UPC), Campus Nord (D3-203), Jordi Girona, 1-3, 08034 Barcelona, Spain
Interests: remote sensing; synthetic aperture radar; polarimetry; interferometry; signal and image processing; quantitative information retrieval
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Prof. Dr. Juanma Lopez Sanchez

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DFISTS – IUII, Universidad de Alicante, P.O. Box 99, E-03080 Alicante, Spain
Interests: radar polarimetry; interferometry; polarimetric SAR interferometry; agriculture; geophysics
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Special Issue Information

Dear Colleagues,

During the last two decades, an increasing number of spaceborne Synthetic Aperture Radar (SAR) systems have been equipped with polarimetric capabilities, as for instance, ALOS and ALOS-2, Radarsat-2, TerraSAR-X, Envisat-ASAR, Sentinel-1a/b, etc. Future planned mission will still present this type of diversity where some examples are RCM, SAOCOM and SAOCOM-CS, Cosmo-Skymed 2nd generation or PAZ. In addition, an increasing number of airborne and even ground-based SAR systems are adopting polarimetric capabilities.

As it has been demonstrated extensively in the past, polarimetry makes it possible to have sensitivity to the structural and geometric properties of the targets under observation, allowing a more accurate identification and classification than non-polarimetric systems. Then, polarimetry has made possible new applications, especially in quantitative extraction of new bio and geophysical parameters. It has been also shown that the combination of polarimetry and interferometry makes it possible an unprecedented sensitivity to the vertical structure of semi-transparent media, such as crops or forests.

Consequently, SAR polarimetry has been an active and fruitful field of research in Earth observation. Besides the development of applications, many researchers have also focused their efforts in theoretical aspects or physical modelling to make SAR polarimetry a truly operative remote sensing technique.

The aim of this Special Issue is to present the state of the art in SAR Polarimetry, ranging from theory and physical modeling to final applications, but also to show the current and futures challenges of SAR Polarimetry with the availability of new sources of data. Therefore, this Special Issue puts also the emphasis on studies for the exploitation of data provided by the new polarimetric space borne SAR sensors, which include additional frequency bands, interferometric capability, enlarged spatial coverage, high spatial resolution and/or shorter revisit times.

This Special Issue of the journal Applied Sciences, “Polarimetric SAR Techniques and Applications”, aims to attract novel contributions covering a wide range of aspects related to PolSAR, from theory and techniques to applications. Our topics of interest include, but are not limited to:
• Fundamental theory of SAR polarimetry
• New processing techniques for PolSAR data: calibration, filtering, classification, etc.
• New or improved target decompositions theorems for PolSAR data,
• Operational or future applications of PolSAR data,
• Combination of PolSAR with interferometry, differential interferometry or other sources of diversity,
• Time series exploitation and change detection based on PolSAR data,
• Data fusion among PolSAR sensors or with other type of data.

Prof. Juan M. Lopez-Sanchez
Prof. Carlos Lopez-Martinez
Guest Editors

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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. Applied Sciences 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 2400 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.

Published Papers (10 papers)

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Editorial

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157 KiB

Open AccessEditorial

Special Issue on Polarimetric SAR Techniques and Applications

by Carlos Lopez-Martinez and Juan M. Lopez-Sanchez

Appl. Sci. 2017, 7(8), 768; https://doi.org/10.3390/app7080768 - 28 Jul 2017

Cited by 2 | Viewed by 3069

Abstract

Synthetic Aperture Radar (SAR) polarimetry is an active and fruitful field of research in Earth observation. [...]
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(This article belongs to the Special Issue Polarimetric SAR Techniques and Applications)

Research

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5880 KiB

Open AccessArticle

Synergetic of PALSAR-2 and Sentinel-1A SAR Polarimetry for Retrieving Aboveground Biomass in Dipterocarp Forest of Malaysia

by Hamdan Omar, Muhamad Afizzul Misman and Abd Rahman Kassim

Appl. Sci. 2017, 7(7), 675; https://doi.org/10.3390/app7070675 - 30 Jun 2017

Cited by 57 | Viewed by 7687

Abstract

Space borne synthetic aperture radar (SAR) data have become one of the primary sources for aboveground biomass (AGB) estimation of forests. However, studies have indicated that limitations occur when a single sensor system is employed, especially in tropical forests. Hence, there is potential for improving estimates if two or more different sensor systems are used. Studies on integrating multiple sensor systems for estimation of AGB over Malaysia’s tropical forests are scarce. This study investigated the use of PALSAR-2 L-band and Sentinel-1A C-band SAR polarizations to estimates the AGB over 5.25 million ha of the lowland, hill, and upper hill forests in Peninsular Malaysia. Polarized images, i.e., HH–HV from PALSAR-2 and VV–VH from Sentinel-1A have been utilized to produce several variables for predictions of the AGB. Simple linear and multiple linear regression analysis was performed to identify the best predictor. The study concluded that although limitations exist in the estimates, the combination of all polarizations from both PALSAR-2 and Sentiel-1A SAR data able to increase the accuracy and reduced the root means square error (RMSE) up to 14 Mg ha⁻¹ compared to the estimation resulted from single polarization. A spatially distributed map of AGB reported the total AGB within the study area was about 1.82 trillion Mg of the year 2016. Full article

(This article belongs to the Special Issue Polarimetric SAR Techniques and Applications)

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2857 KiB

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Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information

by Xianyuan Wang, Zongjie Cao, Yao Ding and Jilan Feng

Appl. Sci. 2017, 7(6), 612; https://doi.org/10.3390/app7060612 - 13 Jun 2017

Cited by 4 | Viewed by 4176

Abstract

The composite kernel feature fusion proposed in this paper attempts to solve the problem of classifying polarimetric synthetic aperture radar (PolSAR) images. Here, PolSAR images take into account both polarimetric and spatial information. Various polarimetric signatures are collected to form the polarimetric feature space, and the morphological profile (MP) is used for capturing spatial information and constructing the spatial feature space. The main idea is that the composite kernel method encodes diverse information within a new kernel matrix and tunes the contribution of different types of features. A support vector machine (SVM) is used as the classifier for PolSAR images. The proposed approach is tested on a Flevoland PolSAR data set and a San Francisco Bay data set, which are in fine quad-pol mode. For the Flevoland PolSAR data set, the overall accuracy and kappa coefficient of the proposed method, compared with the traditional method, increased from 95.7% to 96.1% and from 0.920 to 0.942, respectively. For the San Francisco Bay data set, the overall accuracy and kappa coefficient of the proposed method increased from 92.6% to 94.4% and from 0.879 to 0.909, respectively. Experimental results verify the benefits of using both polarimetric and spatial information via composite kernel feature fusion for the classification of PolSAR images. Full article

(This article belongs to the Special Issue Polarimetric SAR Techniques and Applications)

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3182 KiB

Open AccessArticle

A Multi-Year Study on Rice Morphological Parameter Estimation with X-Band Polsar Data

by Onur Yuzugullu, Esra Erten and Irena Hajnsek

Appl. Sci. 2017, 7(6), 602; https://doi.org/10.3390/app7060602 - 09 Jun 2017

Cited by 12 | Viewed by 3627

Abstract

Rice fields have been monitored with spaceborne Synthetic Aperture Radar (SAR) systems for decades. SAR is an essential source of data and allows for the estimation of plant properties such as canopy height, leaf area index, phenological phase, and yield. However, the information on detailed plant morphology in meter-scale resolution is necessary for the development of better management practices. This letter presents the results of the procedure that estimates the stalk height, leaf length and leaf width of rice fields from a copolar X-band TerraSAR-X time series data based on a priori phenological phase. The methodology includes a computationally efficient stochastic inversion algorithm of a metamodel that mimics a radiative transfer theory-driven electromagnetic scattering (EM) model. The EM model and its metamodel are employed to simulate the backscattering intensities from flooded rice fields based on their simplified physical structures. The results of the inversion procedure are found to be accurate for cultivation seasons from 2013 to 2015 with root mean square errors less than 13.5 cm for stalk height, 7 cm for leaf length, and 4 mm for leaf width parameters. The results of this research provided new perspectives on the use of EM models and computationally efficient metamodels for agriculture management practices. Full article

(This article belongs to the Special Issue Polarimetric SAR Techniques and Applications)

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11693 KiB

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Scattering Characteristics of X-, C- and L-Band PolSAR Data Examined for the Tundra Environment of the Tuktoyaktuk Peninsula, Canada

by Tobias Ullmann, Sarah N. Banks, Andreas Schmitt and Thomas Jagdhuber

Appl. Sci. 2017, 7(6), 595; https://doi.org/10.3390/app7060595 - 08 Jun 2017

Cited by 21 | Viewed by 5496

Abstract

In this study, polarimetric Synthetic Aperture Radar (PolSAR) data at X-, C- and L-Bands, acquired by the satellites: TerraSAR-X (2011), Radarsat-2 (2011), ALOS (2010) and ALOS-2 (2016), were used to characterize the tundra land cover of a test site located close to the town of Tuktoyaktuk, NWT, Canada. Using available in situ ground data collected in 2010 and 2012, we investigate PolSAR scattering characteristics of common tundra land cover classes at X-, C- and L-Bands. Several decomposition features of quad-, co-, and cross-polarized data were compared, the correlation between them was investigated, and the class separability offered by their different feature spaces was analyzed. Certain PolSAR features at each wavelength were sensitive to the land cover and exhibited distinct scattering characteristics. Use of shorter wavelength imagery (X and C) was beneficial for the characterization of wetland and tundra vegetation, while L-Band data highlighted differences of the bare ground classes better. The Kennaugh Matrix decomposition applied in this study provided a unified framework to store, process, and analyze all data consistently, and the matrix offered a favorable feature space for class separation. Of all elements of the quad-polarized Kennaugh Matrix, the intensity based elements K0, K1, K2, K3 and K4 were found to be most valuable for class discrimination. These elements contributed to better class separation as indicated by an increase of the separability metrics squared Jefferys Matusita Distance and Transformed Divergence. The increase in separability was up to 57% for Radarsat-2 and up to 18% for ALOS-2 data. Full article

(This article belongs to the Special Issue Polarimetric SAR Techniques and Applications)

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7080 KiB

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Texture Analysis and Land Cover Classification of Tehran Using Polarimetric Synthetic Aperture Radar Imagery

by Homa Zakeri, Fumio Yamazaki and Wen Liu

Appl. Sci. 2017, 7(5), 452; https://doi.org/10.3390/app7050452 - 29 Apr 2017

Cited by 47 | Viewed by 8892

Abstract

Land cover classification of built-up and bare land areas in arid or semi-arid regions from multi-spectral optical images is not simple, due to the similarity of the spectral characteristics of the ground and building materials. However, synthetic aperture radar (SAR) images could overcome this issue because of the backscattering dependency on the material and the geometry of different surface objects. Therefore, in this paper, dual-polarized data from ALOS-2 PALSAR-2 (HH, HV) and Sentinel-1 C-SAR (VV, VH) were used to classify the land cover of Tehran city, Iran, which has grown rapidly in recent years. In addition, texture analysis was adopted to improve the land cover classification accuracy. In total, eight texture measures were calculated from SAR data. Then, principal component analysis was applied, and the first three components were selected for combination with the backscattering polarized images. Additionally, two supervised classification algorithms, support vector machine and maximum likelihood, were used to detect bare land, vegetation, and three different built-up classes. The results indicate that land cover classification obtained from backscatter values has better performance than that obtained from optical images. Furthermore, the layer stacking of texture features and backscatter values significantly increases the overall accuracy. Full article

(This article belongs to the Special Issue Polarimetric SAR Techniques and Applications)

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3647 KiB

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Dual-Branch Deep Convolution Neural Network for Polarimetric SAR Image Classification

by Fei Gao, Teng Huang, Jun Wang, Jinping Sun, Amir Hussain and Erfu Yang

Appl. Sci. 2017, 7(5), 447; https://doi.org/10.3390/app7050447 - 27 Apr 2017

Cited by 83 | Viewed by 10526

Abstract

The deep convolution neural network (CNN), which has prominent advantages in feature learning, can learn and extract features from data automatically. Existing polarimetric synthetic aperture radar (PolSAR) image classification methods based on the CNN only consider the polarization information of the image, instead of incorporating the image’s spatial information. In this paper, a novel method based on a dual-branch deep convolution neural network (Dual-CNN) is proposed to realize the classification of PolSAR images. The proposed method is built on two deep CNNs: one is used to extract the polarization features from the 6-channel real matrix (6Ch) which is derived from the complex coherency matrix. The other is utilized to extract the spatial features of a Pauli RGB (Red Green Blue) image. These extracted features are first combined into a fully connected layer sharing the polarization and spatial property. Then, the Softmax classifier is employed to classify these features. The experiments are conducted on the Airborne Synthetic Aperture Radar (AIRSAR) data of Flevoland and the results show that the classification accuracy on 14 types of land cover is up to 98.56%. Such results are promising in comparison with other state-of-the-art methods. Full article

(This article belongs to the Special Issue Polarimetric SAR Techniques and Applications)

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4043 KiB

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A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data

by Dongfang Lin, Jianjun Zhu, Haiqiang Fu, Qinghua Xie and Bing Zhang

Appl. Sci. 2017, 7(5), 435; https://doi.org/10.3390/app7050435 - 25 Apr 2017

Cited by 5 | Viewed by 4312

Abstract

The random volume over ground (RVoG) model associates vegetation vertical structure parameters with multiple complex interferometric coherence observables. In this paper, on the basis of the RVoG model, a truncated singular value decomposition (TSVD)-based method is proposed for forest height inversion from single-baseline polarimetric interferometric synthetic aperture radar (PolInSAR) data. In addition, in order to improve the applicability of TSVD for this issue, a new truncation method is proposed for TSVD. Differing from the traditional three-stage method, the TSVD-based inversion method estimates the pure volume coherence directly from the complex interferometric coherence, and estimates the forest height from the estimated pure volume coherence with a least-squares method. As a result, the TSVD-based method can adjust the contributions of the polarizations in the estimation of the model parameters and avoid the null ground-to-volume ratio assumption. The simulated experiments undertaken in this study confirmed that the TSVD-based method performs better than the three-stage method in forest height inversion. The TSVD-based method was also applied to E-SAR P-band data acquired over the Krycklan Catchment, Sweden, which is covered with mixed pine forest. The results showed that the TSVD-based method improves the root-mean-square error by 48.6% when compared to the three-stage method, which further validates the performance of the TSVD-based method. Full article

(This article belongs to the Special Issue Polarimetric SAR Techniques and Applications)

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Analysis of Dual- and Full-Circular Polarimetric SAR Modes for Rice Phenology Monitoring: An Experimental Investigation through Ground-Based Measurements

by Yuta Izumi, Sevket Demirci, Mohd Zafri Bin Baharuddin, Tomoro Watanabe and Josaphat Tetuko Sri Sumantyo

Appl. Sci. 2017, 7(4), 368; https://doi.org/10.3390/app7040368 - 07 Apr 2017

Cited by 14 | Viewed by 5609

Abstract

Circularly polarized synthetic aperture radar (CP-SAR) is known to be insensitive to polarization mismatch losses caused by the Faraday rotation effect and antenna misalignment. Additionally, the dual-circular polarimetric (DCP) mode has proven to have more polarimetric information than that of the corresponding mode of linear polarization, i.e., the dual-linear polarimetric (DLP) mode. Owing to these benefits, this paper investigates the feasibility of CP-SAR for rice monitoring. A ground-based CP-radar system was exploited, and C-band anechoic chamber data of a self-cultivated Japanese rice paddy were acquired from germination to ripening stages. Temporal variations of polarimetric observables derived from full-circular polarimetric (FCP) and DCP as well as synthetically generated DLP data are analyzed and assessed with regard to their effectiveness in phenology retrieval. Among different observations, the

H

\bar{α}

plane and triangle plots obtained by three scattering components (surface, double-bounce, and volume scattering) for both the FCP and DCP modes are confirmed to have reasonable capability in discriminating the relevant intervals of rice growth. Full article

(This article belongs to the Special Issue Polarimetric SAR Techniques and Applications)

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Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images

by Yuanzhi Zhang, Yu Li, X. San Liang and Jinyeu Tsou

Appl. Sci. 2017, 7(2), 193; https://doi.org/10.3390/app7020193 - 16 Feb 2017

Cited by 32 | Viewed by 5798

Abstract

In this paper, we present a comparison between several algorithms for oil spill classifications using fully and compact polarimetric SAR images. Oil spill is considered as one of the most significant sources of marine pollution. As a major difficulty of SAR-based oil spill detection algorithms is the classification between mineral and biogenic oil, we focus on quantitatively analyzing and comparing fully and compact polarimetric satellite synthetic aperture radar (SAR) modes to detect hydrocarbon slicks over the sea surface, discriminating them from weak-damping surfactants, such as biogenic slicks. The experiment was conducted on quad-pol SAR data acquired during the Norwegian oil-on-water experiment in 2011. A universal procedure was used to extract the features from quad-, dual- and compact polarimetric SAR modes to rank different polarimetric SAR modes and common supervised classifiers. Among all the dual- and compact polarimetric SAR modes, the π/2 mode has the best performance. The best supervised classifiers vary and depended on whether sufficient polarimetric information can be obtained in each polarimetric mode. We also analyzed the influence of the number of polarimetric parameters considered as inputs for the supervised classifiers, onto the detection/discrimination performance. We discovered that a feature set with four features is sufficient for most polarimetric feature-based oil spill classifications. Moreover, dimension reduction algorithms, including principle component analysis (PCA) and the local linear embedding (LLE) algorithm, were employed to learn low dimensional and distinctive information from quad-polarimetric SAR features. The performance of the new feature sets has comparable performance in oil spill classification. Full article

(This article belongs to the Special Issue Polarimetric SAR Techniques and Applications)

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