Special Issue "Polarimetric SAR Techniques and Applications"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computer Science and Electrical Engineering".

Deadline for manuscript submissions: closed (15 April 2017)

Special Issue Editors

Guest Editor
Prof. Dr. Carlos López-Martínez

Signal Theory and Communications Dept.,Universitat Politècnica de Catalunya—Bardelona Tech. (UPC), Campus Nord (D3-203), Jordi Girona, 1-3, 08034 Barcelona, Spain
Website | E-Mail
Phone: +34-93-401-6785
Interests: remote sensing; synthetic aperture radar; polarimetry; interferometry; signal and image processing; quantitative information retrieval
Guest Editor
Prof. Dr. Juan Manuel Lopez-Sanchez

DFISTS - IUII, Universidad de Alicante, P.O.Box 99, E-03080 Alicante, Spain
Website | E-Mail
Phone: +34 965909597
Fax: +34 965909750
Interests: polarimetry; interferometry; polarimetric SAR interferometry; agriculture; subsidence

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

Manuscript Submission Information

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Published Papers (5 papers)

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Research

Open AccessArticle Texture Analysis and Land Cover Classification of Tehran Using Polarimetric Synthetic Aperture Radar Imagery
Appl. Sci. 2017, 7(5), 452; doi:10.3390/app7050452 (registering DOI)
Received: 9 March 2017 / Revised: 20 April 2017 / Accepted: 21 April 2017 / Published: 29 April 2017
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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
[...] Read more.
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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Open AccessArticle Dual-Branch Deep Convolution Neural Network for Polarimetric SAR Image Classification
Appl. Sci. 2017, 7(5), 447; doi:10.3390/app7050447
Received: 9 March 2017 / Revised: 23 April 2017 / Accepted: 24 April 2017 / Published: 27 April 2017
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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
[...] Read more.
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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Open AccessArticle A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data
Appl. Sci. 2017, 7(5), 435; doi:10.3390/app7050435
Received: 28 January 2017 / Revised: 20 April 2017 / Accepted: 21 April 2017 / Published: 25 April 2017
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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
[...] Read more.
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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Open AccessArticle Analysis of Dual- and Full-Circular Polarimetric SAR Modes for Rice Phenology Monitoring: An Experimental Investigation through Ground-Based Measurements
Appl. Sci. 2017, 7(4), 368; doi:10.3390/app7040368
Received: 23 February 2017 / Revised: 31 March 2017 / Accepted: 4 April 2017 / Published: 7 April 2017
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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
[...] Read more.
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 / 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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Open AccessArticle Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
Appl. Sci. 2017, 7(2), 193; doi:10.3390/app7020193
Received: 25 December 2016 / Revised: 7 February 2017 / Accepted: 8 February 2017 / Published: 16 February 2017
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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
[...] Read more.
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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