Special Issue "Advances in SAR Image Processing and Applications"

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

Deadline for manuscript submissions: 1 February 2022.

Special Issue Editors

Dr. Jean-Christophe Cexus
E-Mail Website
Guest Editor
Lab-STICC, UMR CNRS 6285, ENSTA Bretagne, 29806 Brest, France
Interests: signal and image processing; machine learning; computer science; engineering; remote sensing; radar
Prof. Dr. Ali Khenchaf
E-Mail Website
Guest Editor
Lab-STICC, UMR CNRS 6285, ENSTA Bretagne, 29806 Brest, France
Interests: computer science; engineering; observation; propagation; wave scattering; scattering in random media; monostatic and bistatic scattering; electromagnetic radar cross section; sea clutter; active and passive sensors (Radar, Lidar, Optics, GNSS); radar applications; data assimilation (n-D); sea surface and environment; extraction of parameters from the observed scene: imagery and target parameter estimation; direct and inverse problems; remote sensing of the ocean and the environment
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Special Issue Information

Dear Colleagues,

In recent decades, an unprecedented amount of data have been gathered by the radar remote sensing community which are boosting the development of an increasing number of applications for the analysis of our environment.

This is due to the ability of radar sensors to operate independently of solar illuminations and penetrate clouds. In addition to that, interest in these sensors stems from their ability to analyze, detect, localize, and identify the slightest changes in the environment (maritime, terrestrial, urban area, etc.).

These tools have been improved with the synthetic aperture radar (SAR) technique. Thanks to this technique, it is not surprising that radar image processing is commonly used for the study of our Earth (urban planning, environmental sciences, earthquakes, hydrology, littoral zones, oceans, etc.) , and also in the problems of the automatic recognition of targets (ATR) in a heterogeneous environment which is not just terrestrial, but also maritime or aerial to provide, for example, a detailed of the battlefield situational awareness (tanks, howitzers, armored personnel carriers, ships, planes, etc.).

In this Special Issue on SAR Image Processing, we propose an overview of technological and scientific advances in synthetic aperture radar (SAR) image processing and applications. Authors are encouraged to present contributions on the exploitation of satellite or airborne SAR images in various problems. Advanced processing techniques such as Interferometric SAR (InSAR), differential InSAR, or polarimetric SAR imaging are also included.

Therefore, we encourage you to submit your recent and innovative work done in the field of this Special Issue dedicated to “Advances in SAR Image Processing and Applications”.

Dr. Jean-Christophe Cexus
Prof. Dr. Ali Khenchaf
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 papers will be 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 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.

Keywords

  • Synthetic aperture radar (SAR)
  • Airborne and satellite systems
  • Signal and image processing
  • Machine (deep) learning, compressive sensing
  • Environment monitoring (maritime, terrestrial, urban area, etc.)
  • Applications (remote sensing, target recognition, target detection, target tracking, target location, pollution detection, tomography, etc.)

Published Papers (3 papers)

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Research

Article
A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
Remote Sens. 2021, 13(15), 2866; https://doi.org/10.3390/rs13152866 - 22 Jul 2021
Viewed by 291
Abstract
Accurate and efficient image mosaicking is essential for generating wide-range swath images of spaceborne scanning synthetic aperture radar (ScanSAR). However, the existing methods cannot guarantee the accuracy and efficiency of stitching simultaneously, especially when mosaicking multiple large-area images. In this paper, we propose [...] Read more.
Accurate and efficient image mosaicking is essential for generating wide-range swath images of spaceborne scanning synthetic aperture radar (ScanSAR). However, the existing methods cannot guarantee the accuracy and efficiency of stitching simultaneously, especially when mosaicking multiple large-area images. In this paper, we propose a novel image mosaic method based on homography matrix compensation to solve the mentioned problem. A set of spaceborne ScanSAR images from the Gaofen-3 (GF-3) satellite were selected to test the performance of the new method. First, images are preprocessed by an improved Wallis filter to eliminate intensity inconsistencies. Then, to reduce the enormous computational redundancy of registration, the overlapping areas of adjacent images are coarsely extracted using geolocation technologies. Furthermore, to improve the efficiency of stitching and maintain the original information and resolution of images, we deduce a compensation of homography matrix to implement downsampled images registration and original-size images projection. After stitching, the transitions at the edges of the images were smooth and seamless, the information and resolution of the original images were preserved successfully, and the efficiency of the mosaic was improved by approximately one thousand-fold. The validity, high efficiency and reliability of the method are verified. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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Article
Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism
Remote Sens. 2021, 13(14), 2686; https://doi.org/10.3390/rs13142686 - 08 Jul 2021
Viewed by 308
Abstract
The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train the network. Moreover, some clutter is very similar to [...] Read more.
The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train the network. Moreover, some clutter is very similar to targets in SAR images with complex scenes, making the target detection task very difficult. Therefore, a SAR target detection network based on a semi-supervised learning and attention mechanism is proposed in this paper. Since the image-level label simply marks whether the image contains the target of interest or not, which is easier to be labeled than the target-level label, the proposed method uses a small number of target-level labeled training samples and a large number of image-level labeled training samples to train the network with a semi-supervised learning algorithm. The proposed network consists of a detection branch and a scene recognition branch with a feature extraction module and an attention module shared between these two branches. The feature extraction module can extract the deep features of the input SAR images, and the attention module can guide the network to focus on the target of interest while suppressing the clutter. During the semi-supervised learning process, the target-level labeled training samples will pass through the detection branch, while the image-level labeled training samples will pass through the scene recognition branch. During the test process, considering the help of global scene information in SAR images for detection, a novel coarse-to-fine detection procedure is proposed. After the coarse scene recognition determining whether the input SAR image contains the target of interest or not, the fine target detection is performed on the image that may contain the target. The experimental results based on the measured SAR dataset demonstrate that the proposed method can achieve better performance than the existing methods. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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Article
Fast SAR Autofocus Based on Ensemble Convolutional Extreme Learning Machine
Remote Sens. 2021, 13(14), 2683; https://doi.org/10.3390/rs13142683 - 07 Jul 2021
Viewed by 336
Abstract
Inaccurate Synthetic Aperture Radar (SAR) navigation information will lead to unknown phase errors in SAR data. Uncompensated phase errors can blur the SAR images. Autofocus is a technique that can automatically estimate phase errors from data. However, existing autofocus algorithms either have poor [...] Read more.
Inaccurate Synthetic Aperture Radar (SAR) navigation information will lead to unknown phase errors in SAR data. Uncompensated phase errors can blur the SAR images. Autofocus is a technique that can automatically estimate phase errors from data. However, existing autofocus algorithms either have poor focusing quality or a slow focusing speed. In this paper, an ensemble learning-based autofocus method is proposed. Convolutional Extreme Learning Machine (CELM) is constructed and utilized to estimate the phase error. However, the performance of a single CELM is poor. To overcome this, a novel, metric-based combination strategy is proposed, combining multiple CELMs to further improve the estimation accuracy. The proposed model is trained with the classical bagging-based ensemble learning method. The training and testing process is non-iterative and fast. Experimental results conducted on real SAR data show that the proposed method has a good trade-off between focusing quality and speed. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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