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70th Anniversary of SAR: A Themed Issue Dedicated to Carl Atwood Wiley

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 15352

Special Issue Editors

Prof. Dr. Vito Pascazio

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Guest Editor
Dipartimento di Ingegneria, Università degli Studi di Napoli “Parthenope”, 80143 Napoli, Italy
Interests: synthetic aperture radar (SAR) image processing; SAR interferometry; SAR tomography; GB-SAR; ground penetrating radars; through-the-wall imaging, and deep learning techniques to radar and remote sensing imaging
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mengdao Xing

E-Mail Website
Guest Editor
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
Interests: synthetic aperture radar (SAR); inverse SAR; sparse signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Gianfranco Fornaro

E-Mail Website
Guest Editor
Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy (CNR), 80124 Neaples, Italy
Interests: SAR; interferometric and tomographic SAR; inverse SAR
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hanwen Yu

E-Mail Website
Guest Editor
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: InSAR signal processing and application; phase unwrapping; algorithm design; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Synthetic aperture radar (SAR) has become a well-established and powerful microwave remote sensing technology, widely used for Earth remote sensing for more than 30 years. SAR provides high-spatial-resolution, short-temporal-resolution, day-and-night, and weather-independent images which are beneficial to applications including geoscience and climate change research, environmental and Earth system monitoring, 2D and 3D mapping, quantifying surface properties, time series analysis, and planetary exploration.

Recently, more and more SAR missions with different imaging modes have been completed, are in progress, or will be launched soon for different, previously unattainable, Earth observations.

This Special Issue is a tribute to the SAR 70th anniversary and a themed issue dedicated to Carl Atwood Wiley. It aims to invite contributions on the latest developments and advances in the field of SAR for Earth remote sensing. Researchers, scientists, and engineers will share new theoretical and experimental work on SAR instrumentations, techniques, and applications.

  • Latest developments and concepts of SAR system design, signal modeling, simulation, image analysis, e.g., Circular SAR
  • Learning algorithms and models of SAR data for Earth remote sensing (supervised/weakly-supervised/unsupervised)
  • Applications of multisensor, multimodal, and multitemporal SAR data fusion techniques
  • Innovative coherent processing of multitemporal SAR data, e.g., TomoSAR
  • New small and low-cost SAR system design, e.g., uninhabited aerial vehicle (UAV) SAR
  • New techniques on SAR polarimetry ranging from theoretical and physical modelling to data-to-information processing
  • SAR applications on land, oceans, cryosphere, and other fields

Prof. Dr. Vito Pascazio
Prof. Dr. Mengdao Xing
Dr. Gianfranco Fornaro
Prof. Dr. Hanwen Yu
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. Sensors 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 2600 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

  • microwave remote sensing
  • synthetic aperture radar (SAR)
  • geoscience

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Published Papers (4 papers)

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Research

19 pages, 7766 KiB  
Article
Staring Spotlight SAR with Nonlinear Frequency Modulation Signal and Azimuth Non-Uniform Sampling for Low Sidelobe Imaging
by Wei Xu, Lu Zhang, Chonghua Fang, Pingping Huang, Weixian Tan and Yaolong Qi
Sensors 2021, 21(19), 6487; https://doi.org/10.3390/s21196487 - 28 Sep 2021
Cited by 2 | Viewed by 2738
Abstract
In synthetic aperture radar (SAR) imaging, geometric resolution, sidelobe level (SLL) and signal-to-noise ratio (SNR) are the most important parameters for measuring the SAR image quality. The staring spotlight mode continuously transmits signals to a fixed area by steering the azimuth beam to [...] Read more.
In synthetic aperture radar (SAR) imaging, geometric resolution, sidelobe level (SLL) and signal-to-noise ratio (SNR) are the most important parameters for measuring the SAR image quality. The staring spotlight mode continuously transmits signals to a fixed area by steering the azimuth beam to acquire azimuth high geometric resolution, and its two-dimensional (2D) impulse response with the low SLL is usually obtained from the 2D weighted power spectral density (PSD) by the selected weighting window function. However, this results in the SNR reduction due to 2D amplitude window weighting. In this paper, the staring spotlight SAR with nonlinear frequency modulation (NLFM) signal and azimuth non-uniform sampling (ANUS) is proposed to obtain high geometric resolution SAR images with the low SLL and almost without any SNR reduction. The NLFM signal obtains non-equal interval frequency sampling points under uniform time sampling by adjusting the instantaneous chirp rate. Its corresponding PSD is similar to the weighting window function, and its pulse compression result without amplitude window weighting has low sidelobes. To obtain a similar Doppler frequency distribution for low sidelobe imaging in azimuth, the received SAR echoes are designed to be non-uniformly sampled in azimuth, in which the sampling sequence is dense in middle and sparse in both ends, and azimuth compression result with window weighting would also have low sidelobes. According to the echo model of the proposed imaging mode, both the back projection algorithm (BPA) and range migration algorithm (RMA) are modified and presented to handle the raw data of the proposed imaging mode. Both imaging results on simulated targets and experimental real SAR data processing results of a ground-based radar validate the proposed low sidelobe imaging mode. Full article
(This article belongs to the Special Issue 70th Anniversary of SAR: A Themed Issue Dedicated to Carl Atwood Wiley)
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15 pages, 4080 KiB  
Article
CNN-LRP: Understanding Convolutional Neural Networks Performance for Target Recognition in SAR Images
by Bo Zang, Linlin Ding, Zhenpeng Feng, Mingzhe Zhu, Tao Lei, Mengdao Xing and Xianda Zhou
Sensors 2021, 21(13), 4536; https://doi.org/10.3390/s21134536 - 1 Jul 2021
Cited by 17 | Viewed by 5941
Abstract
Target recognition is one of the most challenging tasks in synthetic aperture radar (SAR) image processing since it is highly affected by a series of pre-processing techniques which usually require sophisticated manipulation for different data and consume huge calculation resources. To alleviate this [...] Read more.
Target recognition is one of the most challenging tasks in synthetic aperture radar (SAR) image processing since it is highly affected by a series of pre-processing techniques which usually require sophisticated manipulation for different data and consume huge calculation resources. To alleviate this limitation, numerous deep-learning based target recognition methods are proposed, particularly combined with convolutional neural network (CNN) due to its strong capability of data abstraction and end-to-end structure. In this case, although complex pre-processing can be avoided, the inner mechanism of CNN is still unclear. Such a “black box” only tells a result but not what CNN learned from the input data, thus it is difficult for researchers to further analyze the causes of errors. Layer-wise relevance propagation (LRP) is a prevalent pixel-level rearrangement algorithm to visualize neural networks’ inner mechanism. LRP is usually applied in sparse auto-encoder with only fully-connected layers rather than CNN, but such network structure usually obtains much lower recognition accuracy than CNN. In this paper, we propose a novel LRP algorithm particularly designed for understanding CNN’s performance on SAR image target recognition. We provide a concise form of the correlation between output of a layer and weights of the next layer in CNNs. The proposed method can provide positive and negative contributions in input SAR images for CNN’s classification, viewed as a clear visual understanding of CNN’s recognition mechanism. Numerous experimental results demonstrate the proposed method outperforms common LRP. Full article
(This article belongs to the Special Issue 70th Anniversary of SAR: A Themed Issue Dedicated to Carl Atwood Wiley)
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19 pages, 2570 KiB  
Article
Multi-Aspect SAR Target Recognition Based on Prototypical Network with a Small Number of Training Samples
by Pengfei Zhao, Lijia Huang, Yu Xin, Jiayi Guo and Zongxu Pan
Sensors 2021, 21(13), 4333; https://doi.org/10.3390/s21134333 - 24 Jun 2021
Cited by 7 | Viewed by 2715
Abstract
At present, synthetic aperture radar (SAR) automatic target recognition (ATR) has been deeply researched and widely used in military and civilian fields. SAR images are very sensitive to the azimuth aspect of the imaging geomety; the same target at different aspects differs greatly. [...] Read more.
At present, synthetic aperture radar (SAR) automatic target recognition (ATR) has been deeply researched and widely used in military and civilian fields. SAR images are very sensitive to the azimuth aspect of the imaging geomety; the same target at different aspects differs greatly. Thus, the multi-aspect SAR image sequence contains more information for classification and recognition, which requires the reliable and robust multi-aspect target recognition method. Nowadays, SAR target recognition methods are mostly based on deep learning. However, the SAR dataset is usually expensive to obtain, especially for a certain target. It is difficult to obtain enough samples for deep learning model training. This paper proposes a multi-aspect SAR target recognition method based on a prototypical network. Furthermore, methods such as multi-task learning and multi-level feature fusion are also introduced to enhance the recognition accuracy under the case of a small number of training samples. The experiments by using the MSTAR dataset have proven that the recognition accuracy of our method can be close to the accruacy level by all samples and our method can be applied to other feather extraction models to deal with small sample learning problems. Full article
(This article belongs to the Special Issue 70th Anniversary of SAR: A Themed Issue Dedicated to Carl Atwood Wiley)
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17 pages, 4777 KiB  
Article
Ultra-High Resolution Imaging Method for Distributed Small Satellite Spotlight MIMO-SAR Based on Sub-Aperture Image Fusion
by Fang Zhou, Jun Yang, Lu Jia, Xingming Yang and Mengdao Xing
Sensors 2021, 21(5), 1609; https://doi.org/10.3390/s21051609 - 25 Feb 2021
Cited by 6 | Viewed by 2279
Abstract
Small satellite synthetic aperture radar (SAR) has become a new development direction of spaceborne SAR due to its advantages of flexible launch, short development cycle, and low cost. However, there are fewer researches on distributed small satellite multiple input multiple output (MIMO) SAR. [...] Read more.
Small satellite synthetic aperture radar (SAR) has become a new development direction of spaceborne SAR due to its advantages of flexible launch, short development cycle, and low cost. However, there are fewer researches on distributed small satellite multiple input multiple output (MIMO) SAR. This paper proposes an ultra-high resolution imaging method for the distributed small satellite spotlight MIMO-SAR, which applies the sub-aperture division technique and the sub-aperture image coherent fusion algorithm to MIMO-SAR. After deblurring the sub-aperture signal, the large bandwidth signal is obtained by using an improved time domain bandwidth synthesis (TBS) method, and then the ultra-high resolution image is obtained by using a sub-aperture image coherent fusion algorithm. Simulation results validate the feasibility and effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue 70th Anniversary of SAR: A Themed Issue Dedicated to Carl Atwood Wiley)
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