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Emerging Techniques and Applications of Polarimetric SAR (PolSAR)

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 19855

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Special Issue Editors

Dr. Luis Gómez Déniz

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Guest Editor

Department of Electronic Engineering and Automatic Control, Image Technology Center (CTIM), University of Las Palmas de Gran Canaria, 35017 Las Pamas, Spain
Interests: remote sensing; SAR/PolSAR; speckle; statistical modelling; computer vision
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Raydonal Ospina

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Guest Editor

Department of Statistics, CastLab, Federal University of Pernambuco, Recife/PE 50740–540, Brazil
Interests: speckle; statistical learning; SAR; signal and image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

SAR (synthetic aperture radar) and PolSAR (polarimetric SAR) systems are powerful remote sensing systems able to monitor the whole planet at unprecedented levels of precision to provide highly valuable information. Such systems offer huge data to researchers and to final users to assist on monitoring/planning land information: urban areas, land cover (deforestation, cover vegetation, soil moisture), and also retrieving oceanic information (oil spills detection) and water resources, among other applications of interest.

In order to fully extract information from the data, new methods and strategies are strongly required. Fortunately, computational capabilities have also experimented on an increase in their capabilities, allowing to process data in a more efficient way through multicore/GPU resources. In that sense, the extraordinary potential already shown by the CNNs (convolutional neural networks) demands special attention on new methods.

This Special Issue focuses on exploring new techniques for the data-to-information process for SAR/PolSAR systems. Pattern recognition and machine learning methods built on suitable statistical models closely linked to the data are the main interest of this Special Issue, although it is also open to theoretical and physical SAR/PolSAR models.

For this Special Issue, we invite submissions on, but not limited to, the following topics:

Statistical models for SAR/PolSAR data;
Machine learning and CNNs methods for SAR/PolSAR data;
Modern classification/segmentation methods;
Statistical signal processing of SAR/PolSAR data;
Statistical representation of SAR/PolSAR data;
Statistical insights of noise modeling;

Dr. Luis Gómez Déniz
Prof. Dr. Raydonal Ospina
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

PolSAR
Statistical models
Image enhancement
Environmental monitoring
Data representation
Artificial Intelligence

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Published Papers (6 papers)

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Research

19 pages, 3961 KiB

Open AccessArticle

A Hybrid Polarimetric Target Decomposition Algorithm with Adaptive Volume Scattering Model

by Xiujuan Li, Yongxin Liu, Pingping Huang, Xiaolong Liu, Weixian Tan, Wenxue Fu and Chunming Li

Remote Sens. 2022, 14(10), 2441; https://doi.org/10.3390/rs14102441 - 19 May 2022

Cited by 4 | Viewed by 1896

Abstract

Previous studies have shown that scattering mechanism ambiguity and negative power issues still exist in model-based polarization target decomposition algorithms, even though deorientation processing is implemented. One possible reason for this is that the dynamic range of the model itself is limited and cannot fully satisfy the mixed scenario. To address these problems, we propose a hybrid polarimetric target decomposition algorithm (GRH) with a generalized volume scattering model (GVSM) and a random particle cloud volume scattering model (RPCM). The adaptive volume scattering model used in GRH incorporates GVSM and RPCM to model the volume scattering model of the regions dominated by double-bounce scattering and the surface scattering, respectively, to expand the dynamic range of the model. In addition, GRH selects the volume scattering component between GVSM and RPCM adaptively according to the target dominant scattering mechanism of fully polarimetric synthetic aperture radar (PolSAR) data. The effectiveness of the proposed method was demonstrated using AirSAR dataset over San Francisco. Comparison studies were carried out to test the performance of GRH over several target decomposition algorithms. Experimental results show that the GRH outperforms the algorithms we tested in this study in decomposition accuracy and reduces the number of pixels with negative powers, demonstrating that the GRH can significantly avoid mechanism ambiguity and negative power issues. Full article

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

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18 pages, 31833 KiB

Open AccessArticle

Polarization Optimization for the Detection of Multiple Persistent Scatterers Using SAR Tomography

by Hossein Aghababaei, Giampaolo Ferraioli, Alfred Stein and Luis Gómez Déniz

Remote Sens. 2022, 14(9), 1960; https://doi.org/10.3390/rs14091960 - 19 Apr 2022

Cited by 1 | Viewed by 1878

Abstract

The detection of multiple interfering persistent scatterers (PSs) using Synthetic Aperture Radar (SAR) tomography is an efficient tool for generating point clouds of urban areas. In this context, detection methods based upon the polarization information of SAR data are effective at increasing the number of PSs and producing high-density point clouds. This paper presents a comparative study on the effects of the polarization design of a radar antenna on further improving the probability of detecting persistent scatterers. For this purpose, we introduce an extension of the existing scattering property-based generalized likelihood ratio test (GLRT) with realistic dependence on the transmitted/received polarizations. The test is based upon polarization basis optimization by synthesizing all possible polarimetric responses of a given scatterer from its measurements on a linear orthonormal basis. Experiments on both simulated and real data show, by means of objective metrics (probability of detection, false alarm rate, and signal-to-noise ratio), that polarization waveform optimization can provide a significant performance gain in the detection of multiple scatterers compared to the existing full-polarization-based detection method. In particular, the increased density of detected PSs at the studied test sites demonstrates the main contribution of the proposed method. Full article

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

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22 pages, 7544 KiB

Open AccessArticle

Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

by Hamid Jafarzadeh, Masoud Mahdianpari, Eric Gill, Fariba Mohammadimanesh and Saeid Homayouni

Remote Sens. 2021, 13(21), 4405; https://doi.org/10.3390/rs13214405 - 2 Nov 2021

Cited by 97 | Viewed by 7011

Abstract

In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ attention in data science due to their superior results compared to other commonly used ML algorithms. Despite their popularity within the computer science community, they have not yet been well examined in detail in the field of Earth Observation (EO) for satellite image classification. As such, this study investigates the capability of different EL algorithms, generally known as bagging and boosting algorithms, including Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and Random Forest (RF), for the classification of Remote Sensing (RS) data. In particular, different classification scenarios were designed to compare the performance of these algorithms on three different types of RS data, namely high-resolution multispectral, hyperspectral, and Polarimetric Synthetic Aperture Radar (PolSAR) data. Moreover, the Decision Tree (DT) single classifier, as a base classifier, is considered to evaluate the classification’s accuracy. The experimental results demonstrated that the RF and XGBoost methods for the multispectral image, the LightGBM and XGBoost methods for hyperspectral data, and the XGBoost and RF algorithms for PolSAR data produced higher classification accuracies compared to other ML techniques. This demonstrates the great capability of the XGBoost method for the classification of different types of RS data. Full article

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

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19 pages, 66315 KiB

Open AccessArticle

PolSAR Ship Detection with Optimal Polarimetric Rotation Domain Features and SVM

by Haoliang Li, Xingchao Cui and Siwei Chen

Remote Sens. 2021, 13(19), 3932; https://doi.org/10.3390/rs13193932 - 30 Sep 2021

Cited by 20 | Viewed by 2344

Abstract

Polarimetric synthetic aperture radar (PolSAR) can obtain fully polarimetric information, which provides chances to better understand target scattering mechanisms. Ship detection is an important application of PolSAR and a number of scattering mechanism-based ship detection approaches have been established. However, the backscattering of manmade targets including ships is sensitive to the relative geometry between target orientation and radar line of sight, which makes ship detection still challenging. This work aims at mitigating this issue by target scattering diversity mining and utilization in polarimetric rotation domain with the interpretation tools of polarimetric coherence and correlation pattern techniques. The core idea is to find an optimal combination of polarimetric rotation domain features which shows the best potential to discriminate ship target and sea clutter pixel candidates. With the Relief method, six polarimetric rotation domain features derived from the polarimetric coherence and correlation patterns are selected. Then, a novel ship detection method is developed thereafter with these optimal features and the support vector machine (SVM) classifier. The underlying physics is that ship detection is equivalent to ship and sea clutter classification after the ocean and land partition. Four kinds of spaceborne PolSAR datasets from Radarsat-2 and GF-3 are used for comparison experiments. The superiority of the proposed detection methodology is clearly demonstrated. The proposed method achieves the highest figure of merit (FoM) of 99.26% and 100% for two Radarsat-2 datasets, and of 95.45% and 99.96% for two GF-3 datasets. Specially, the proposed method shows better performance to detect inshore dense ships and reserve the ship structure. Full article

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

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21 pages, 14686 KiB

Open AccessArticle

An Adaptive Decomposition Approach with Dipole Aggregation Model for Polarimetric SAR Data

by Zezhong Wang, Qiming Zeng and Jian Jiao

Remote Sens. 2021, 13(13), 2583; https://doi.org/10.3390/rs13132583 - 1 Jul 2021

Cited by 8 | Viewed by 2155

Abstract

Polarimetric synthetic aperture radar (PolSAR) has attracted lots of attention from remote sensing scientists because of its various advantages, e.g., all-weather, all-time, penetrating capability, and multi-polarimetry. The three-component scattering model proposed by Freeman and Durden (FDD) has bridged the data and observed target with physical scattering model, whose simplicity and practicality have advanced remote sensing applications. However, the three-component scattering model also has some disadvantages, such as negative powers and a scattering model unfitted to observed target, which can be improved by adaptive methods. In this paper, we propose a novel adaptive decomposition approach in which we established a dipole aggregation model to fit every pixel in PolSAR image to an independent volume scattering mechanism, resulting in a reduction of negative powers and an improved adaptive capability of decomposition models. Compared with existing adaptive methods, the proposed approach is fast because it does not utilize any time-consuming algorithm of iterative optimization, is simple because it does not complicate the original three-component scattering model, and is clear for each model being fitted to explicit physical meaning, i.e., the determined adaptive parameter responds to the scattering mechanism of observed target. The simulation results indicated that this novel approach reduced the possibility of the occurrence of negative powers. The experiments on ALOS-2 and RADARSAT-2 PolSAR images showed that the increasing of adaptive parameter reflected more effective scatterers aggregating at the 45° direction corresponding to high cross-polarized property, which always appeared in the 45° oriented buildings. Moreover, the random volume scattering model used in the FDD could be expressed by the novel dipole aggregation model with an adaptive parameter equal to one that always appeared in the forest area. Full article

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

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23 pages, 82922 KiB

Open AccessArticle

Comparison of Target Detectors to Identify Icebergs in Quad-Polarimetric L-Band Synthetic Aperture Radar Data

by Johnson Bailey, Armando Marino and Vahid Akbari

Remote Sens. 2021, 13(9), 1753; https://doi.org/10.3390/rs13091753 - 30 Apr 2021

Cited by 5 | Viewed by 2644

Abstract

Icebergs represent hazards to ships and maritime activities and therefore their detection is essential. Synthetic Aperture Radar (SAR) satellites are very useful for this, due to their capability to acquire data under cloud cover and during day and night passes. In this work, we compared six state-of-the-art polarimetric target detectors to test their performance and ability to detect small-sized icebergs <120 m in four locations in Greenland. We used four single-look complex (SLC) ALOS-2 quad-polarimetric images from JAXA for quad-polarimetric detection and we compared with dual-polarimetric detectors using only the channels HH and HV. We also compared these detectors with single-polarimetric intensity channels and we tested using two scenarios: open ocean and sea ice. Our results show that the multi-look polarimetric whitening filter (MPWF) and the optimal polarimetric detector (OPD) provide the most optimal performance in quad- and dual-polarimetric mode detection. The analysis shows that, overall, quad-polarimetric detectors provide the best detection performance. When the false alarm rate (P_F) is fixed to 10⁻⁵, the probabilities of detection (P_D) are 0.99 in open ocean and 0.90 in sea ice. Dual-polarimetric or single-polarimetric detectors show an overall reduction in performance (the ROC curves show a decrease), but this degradation is not very large (<0.1) when the value of false alarms is relatively high (i.e., we are interested in bigger icebergs with a brighter backscattering >120 m, as they are easier to detect). However, the differences between quad- and dual- or single-polarimetric detectors became much more evident when the P_F value was fixed to low detection probabilities 10⁻⁶ (i.e., smaller icebergs). In the single-polarimetric mode, the HV channel showed P_D values of 0.62 for open ocean and 0.26 for sea ice, compared to values of 0.81 (open ocean) and 0.77 (sea ice) obtained with quad-polarimetric detectors. Full article

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

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