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Special Issue "New Statistical Approaches for Turning SAR/PolSAR Data into Information"

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 (31 December 2019) | Viewed by 3053

Special Issue Editors

Dr. Luis Gómez Déniz
E-Mail Website
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. Alejandro C. Frery
E-Mail Website
Guest Editor
Universidade Federal de Alagoas, Av. Lourival Melo Mota, s/n, Maceió, AL 57072-900, Brazil
Interests: statistical computing; SAR; PolSAR; speckle; information theory; information geometry
Dr. Gui Gao
E-Mail Website
Guest Editor
Faculty of Geoscience and Environmental Engineering, Southwest Jiaotong University, No. 111, North 1st Section, 2nd Ring Road, Chengdu 611756, Sichuan, China
Interests: SAR; detection; recognition; marine environment; machine learning

Special Issue Information

Dear Colleagues,

In this last decade, research on SAR (Synthetic Aperture Radar) and PolSAR (Polarimetric SAR) systems has received increasing interest, leading to truly innovative applications. Computational capabilities have also supported such development, allowing to better process the large available data provided by the existing SAR and PolSAR satellites and airborne systems.

To transform such daily increasing amount of high-quality, modern, remote sensing data into valuable information, new methods and new strategies are required. In this sense, new statistical models for new high-resolution SAR/PolSAR systems assisting on retrieving land information (soil moisture, cover vegetation, urban areas, ocean surface parameters, target identification) are of maximum interest for both researchers and final users.

For real-time applications, the elaboration of efficient methods to extract significant information from data remains a challenge. This Special Issue focuses on novel techniques regarding the data-to-information process related to SAR/PolSAR systems and on easing their potential applications. It covers a broad and comprehensive series of subjects related to statistical modeling, information theory, machine-learning approaches, data acquisition, and data delivery to users for immediate assimilation. Topics may also include emerging statistical models for signal processing and image interpretation.

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

  • Statistical models for SAR/PolSAR data
  • Modern Classification/Segmentation Methods
  • Information Theory for SAR/PolSAR applications
  • Inference
  • Statistical signal processing of SAR/PolSAR data
  • Machine learning
  • Statistical representation of SAR/PolSAR data
  • Statistical insights of noise modelling
  • Denoising

Dr. Luis Gómez Déniz
Prof. Alejandro C. Frery
Dr. Gui Gao
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 2500 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

  • SAR
  • PolSAR
  • Statistical models
  • Information theory
  • Data representation
  • Image interpretation
  • Signal processing

Published Papers (2 papers)

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Research

Article
Accurate Despeckling and Estimation of Polarimetric Features by Means of a Spatial Decorrelation of the Noise in Complex PolSAR Data
Remote Sens. 2020, 12(2), 331; https://doi.org/10.3390/rs12020331 - 20 Jan 2020
Cited by 10 | Viewed by 1136
Abstract
In this work, we extended a procedure for the spatial decorrelation of fully-developed speckle, originally developed for single-polarization SAR data, to fully-polarimetric SAR data. The spatial correlation of the noise depends on the tapering window in the Fourier domain used by the SAR [...] Read more.
In this work, we extended a procedure for the spatial decorrelation of fully-developed speckle, originally developed for single-polarization SAR data, to fully-polarimetric SAR data. The spatial correlation of the noise depends on the tapering window in the Fourier domain used by the SAR processor to avoid defocusing of targets caused by Gibbs effects. Since each polarimetric channel is focused independently of the others, the noise-whitening procedure can be performed applying the decorrelation stage to each channel separately. Equivalently, the noise-whitening stage is applied to each element of the scattering matrix before any multilooking operation, either coherent or not, is performed. In order to evaluate the impact of a spatial decorrelation of the noise on the performance of polarimetric despeckling filters, we make use of simulated PolSAR data, having user-defined polarimetric features. We optionally introduce a spatial correlation of the noise in the simulated complex data by means of a 2D separable Hamming window in the Fourier domain. Then, we remove such a correlation by using the whitening procedure and compare the accuracy of both despeckling and polarimetric features estimation for the three following cases: uncorrelated, correlated, and decorrelated images. Simulation results showed a steady improvement of performance scores, most notably the equivalent number of looks (ENL), which increased after decorrelation and closely attained the value of the uncorrelated case. Besides ENL, the benefits of the noise decorrelation hold also for polarimetric features, whose estimation accuracy is diminished by the correlation. Also, the trends of simulations were confirmed by qualitative results of experiments carried out on a true Radarsat-2 image. Full article
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Article
Auto Encoder Feature Learning with Utilization of Local Spatial Information and Data Distribution for Classification of PolSAR Image
Remote Sens. 2019, 11(11), 1313; https://doi.org/10.3390/rs11111313 - 01 Jun 2019
Cited by 2 | Viewed by 1674
Abstract
The distribution of data plays a key role in the designing of a machine learning model. Therefore, this paper proposes a novel auto encoder network based on the distribution of polarimetric synthetic aperture radar (PolSAR) data matrix. Designed specifically for PolSAR data matrix, [...] Read more.
The distribution of data plays a key role in the designing of a machine learning model. Therefore, this paper proposes a novel auto encoder network based on the distribution of polarimetric synthetic aperture radar (PolSAR) data matrix. Designed specifically for PolSAR data matrix, the proposed mixture auto encoder (MAE) feature learning method defines data error term in the loss function according to the data distribution. Instead of the pixel itself, all pixels in the neighborhood are used as input to train the proposed MAE. Then, a corresponding classification network is also given by discarding the decoder process of the proposed MAE and connecting with a Softmax classifier. The MAE is trained using the unlabeled data, while the training process of the classification network is completed with the help of a small number of labeled pixels. In view of the phenomenon of misclassification in the predicted result image, two post-processing steps acting on local spatial are also given, which accomplished by the proposed two filters. Extensive experiments by four methods were made over three real PolSAR images including the proposed classification network. The experimental results show that introducing data distribution into the auto encoder network leads to an average 4% improvement in overall accuracy for three PolSAR images. Moreover, the post-processing steps with the proposed filters bring a new level of discrimination on the classification performance of PolSAR images. Full article
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