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Remote Sensing
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Signal Processing of Polarimetric SAR: Detection and Parameter Extraction
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Signal Processing of Polarimetric SAR: Detection and Parameter Extraction

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 (1 October 2020) | Viewed by 12675

Special Issue Editors

Dr. Armando Marino

E-Mail Website
Guest Editor
Biological and Environmental Sciences, Faculty of Natural Sciences, University of Stirling, Stirling FK9 4LA, Scotland, UK
Interests: SAR remote sensing; SAR polarimetry; SAR interferometry; environmental monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Carlos López-Martínez

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. Ferdinando Nunziata

E-Mail Website
Guest Editor
Dipartimento di Ingegneria, Università di Napoli Parthenope, 80133 Napoli, NA, Italy
Interests: synthetic aperture radar for sea observation; microwave radiometry; sea surface scattering; GNSS reflectometry
Special Issues, Collections and Topics in MDPI journals
Prof. Laurent Ferro-Famil

E-Mail Website
Guest Editor
Institute of Electronics and Telecommunications of Rennes, University of Rennes 1, 263, Av. Leclerc Campus Beaulieu, Bât 11D, 35042 Rennes, FranceCESBIO, 18 Av Belin, BPI 2801, 31401 Toulouse, France
Interests: EM and radar imaging; SAR tomography; signal processing; snow remote sensing; inverse problems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Synthetic aperture radar (SAR) is an important remote sensing system. In addition to the well-known capability of SAR to obtain Earth surface data not reliant on solar illumination and almost at any weather condition, SAR allows investigating the inside of several volumetric targets (e.g., forests, crops, ice, dry sand), providing information not accessible by other remote sensing systems. It has been demonstrated that the use of polarimetric SAR (PolSAR) significantly improves the capabilities of single polarization algorithms when it comes to detection and parameter extraction.

This Special Issue is aimed at collecting remarkable works that explore new ways to extract information from PolSAR data. This includes theoretical and applied methodologies aimed at solving current issues when processing of PolSAR data considering different modes (quad-, dual-, compact-mode). We will cover:

  • Detection and change detection theory: detectors and classifiers examining similarities between polarimetric targets using physical, algebraic or statistical methodologies;
  • Systems: Novel PolSAR systems architectures, acquisition modes, etc.;
  • Spectral analysis: algorithms extracting information contained in the spectrum of PolSAR data;
  • Image formation and data quality improvement: polarimetric calibration, speckle filters or other algorithms aimed at improving data quality;
  • Scattering models: empirical, semi-empirical, and deterministic models to improve the understanding of PolSAR data and the extraction of geo- and biophysical parameters (forward and inverse problems). This also includes the use of acquisitions with multiple baselines;
  • Multi-baseline image formation: algorithms concerning the use of multiple baselines to perform tomography or more generally extract parameters from volumetric targets.

Dr. Armando Marino
Dr. Carlos Lopez-Martinez
Dr. Ferdinando Nunziata
Dr. Laurent Ferro-Famil
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)
  • Polarimetry
  • PolSAR
  • target detection
  • change detection
  • spectral analysis
  • multi-baseline approaches
  • tomography
  • bio-physical parameter estimation
  • scattering models

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Published Papers (3 papers)

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26 pages, 12479 KiB  
Article
Quad-Polarimetric Multi-Scale Analysis of Icebergs in ALOS-2 SAR Data: A Comparison between Icebergs in West and East Greenland
by Johnson Bailey and Armando Marino
Remote Sens. 2020, 12(11), 1864; https://doi.org/10.3390/rs12111864 - 9 Jun 2020
Cited by 10 | Viewed by 3714
Abstract
Icebergs are ocean hazards which require extensive monitoring. Synthetic Aperture Radar (SAR) satellites can help with this, however, SAR backscattering is strongly influenced by the properties of icebergs, together with meteorological and environmental conditions. In this work, we used five images of quad-pol [...] Read more.
Icebergs are ocean hazards which require extensive monitoring. Synthetic Aperture Radar (SAR) satellites can help with this, however, SAR backscattering is strongly influenced by the properties of icebergs, together with meteorological and environmental conditions. In this work, we used five images of quad-pol ALOS-2/PALSAR-2 SAR data to analyse 1332 icebergs in five locations in west and east Greenland. We investigate the backscatter and polarimetric behaviour, by using several observables and decompositions such as the Cloude–Pottier eigenvalue/eigenvector and Yamaguchi model-based decompositions. Our results show that those icebergs can contain a variety of scattering mechanisms at L-band. However, the most common scattering mechanism for icebergs is surface scattering, with the second most dominant volume scattering (or more generally, clouds of dipoles). In some cases, we observed a double bounce dominance, but this is not as common. Interestingly, we identified that different locations (e.g., glaciers) produce icebergs with different polarimetric characteristics. We also performed a multi-scale analysis using boxcar 5 × 5 and 11 × 11 window sizes and this revealed that depending on locations (and therefore, characteristics) icebergs can be a collection of strong scatterers that are packed in a denser or less dense way. This gives hope for using quad-pol polarimetry to provide some iceberg classifications in the future. Full article
(This article belongs to the Special Issue Signal Processing of Polarimetric SAR: Detection and Parameter Extraction)
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27 pages, 23440 KiB  
Article
Ship Detection Using Deep Convolutional Neural Networks for PolSAR Images
by Weiwei Fan, Feng Zhou, Xueru Bai, Mingliang Tao and Tian Tian
Remote Sens. 2019, 11(23), 2862; https://doi.org/10.3390/rs11232862 - 2 Dec 2019
Cited by 40 | Viewed by 5119
Abstract
Ship detection plays an important role in many remote sensing applications. However, the performance of the PolSAR ship detection may be degraded by the complicated scattering mechanism, multi-scale size of targets, and random speckle noise, etc. In this paper, we propose a ship [...] Read more.
Ship detection plays an important role in many remote sensing applications. However, the performance of the PolSAR ship detection may be degraded by the complicated scattering mechanism, multi-scale size of targets, and random speckle noise, etc. In this paper, we propose a ship detection method for PolSAR images based on modified faster region-based convolutional neural network (Faster R-CNN). The main improvements include proposal generation by adopting multi-level features produced by the convolution layers, which fits ships with different sizes, and the addition of a Deep Convolutional Neural Network (DCNN)-based classifier for training sample generation and coast mitigation. The proposed method has been validated by four measured datasets of NASA/JPL airborne synthetic aperture radar (AIRSAR) and uninhabited aerial vehicle synthetic aperture radar (UAVSAR). Performance comparison with the modified constant false alarm rate (CFAR) detector and the Faster R-CNN has demonstrated that the proposed method can improve the detection probability while reducing the false alarm rate and missed detections. Full article
(This article belongs to the Special Issue Signal Processing of Polarimetric SAR: Detection and Parameter Extraction)
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23 pages, 31441 KiB  
Letter
Validating GEV Model for Reflection Symmetry-Based Ocean Ship Detection with Gaofen-3 Dual-Polarimetric Data
by Rui Guo, Jingyu Cui, Guobin Jing, Shuangxi Zhang and Mengdao Xing
Remote Sens. 2020, 12(7), 1148; https://doi.org/10.3390/rs12071148 - 3 Apr 2020
Cited by 7 | Viewed by 2887
Abstract
The spaceborne synthetic aperture radar (SAR) is quite powerful in worldwide ocean observation, especially for ship monitoring, as a hot topic in ocean surveillance. The launched Gaofen-3 (GF3) satellite of China can provide C-band and multi-polarization SAR data, and one of its scientific [...] Read more.
The spaceborne synthetic aperture radar (SAR) is quite powerful in worldwide ocean observation, especially for ship monitoring, as a hot topic in ocean surveillance. The launched Gaofen-3 (GF3) satellite of China can provide C-band and multi-polarization SAR data, and one of its scientific applications is ocean ship detection. Compared with the single polarization system, polarimetric systems can be used for more effective ship detection. In this paper, a generalized extreme value (GEV)-based constant false alarm rate (CFAR) detector is proposed for ship detection in the ocean by using the reflection symmetry metric of dual-polarization. The reflection symmetry property shows big differences between the metallic targets at sea and the sea surface. In addition, the GEV statistical model is employed for reflection symmetry statistical distribution, which fits the reflection symmetry probability density function (pdf) well. Five dual-polarimetric GF3 stripmap ocean data sets are introduced in the paper, to show the contrast in enhancement by using reflection symmetry and to investigate the GEV model fit to the reflection symmetry metric. Additionally, with the detection experiments on the real GF3 datasets, the effectiveness and efficiency of the GEV model for reflection symmetry and the model-based ocean ship detector are verified. Full article
(This article belongs to the Special Issue Signal Processing of Polarimetric SAR: Detection and Parameter Extraction)
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