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SAR Data Processing and Applications Based on Machine Learning Method

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

Deadline for manuscript submissions: closed (1 June 2024) | Viewed by 18312

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Special Issue Editors

Prof. Dr. Jie Chen

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Guest Editor

School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
Interests: space-borne SAR signal processsing; SAR image quality improvement; SAR imaging understanding
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Dr. Peng Xiao

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Guest Editor

College of Information Engineering, Capital Normal University, Beijing 100048, China
Interests: new space SAR system design; advanced radar signal processing; polar remote sensing

Dr. Yanan You

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Guest Editor

School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: remote sensing image understing; imaging detection and intelligent perception; trustworthy artificial intelligence
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Dr. Wei Yang

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Guest Editor

School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
Interests: moving target detection; machine learning method on SAR; SAR 3-D imaging

Prof. Dr. John Trinder

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Guest Editor

Surveying and Geospatial Engineering, School of Civil and Environmental Engineering, The University of New South Wales (UNSW), Sydney, NSW 2052, Australia
Interests: photogrammetry and remote sensing; geospatial information systems; SAR remote sensing; feature extraction from images; sustainable development; ecosystem services
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Prof. Dr. Dusan Gleich

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Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, SI-2000 Maribor, Slovenia
Interests: synthetic aperture radar image enhancement; small-radar development; deep learning for SAR image enhancement; data interpretation, short-range radar development, radar signal processing, through the wall imaging, soil moisture estimation, and machine vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the explosive growth of data and great advances in computing power, machine learning techniques are having a profound impact on the development of remote sensing. Taking advantage of this, remote sensing technology is transforming from model-driven to data-driven. Due to the unique nature of SAR signals and images, machine learning SAR still offers many opportunities for exploration, which is also an exciting challenge for us. The unique non-linear capability of machine learning has revealed the further potential of SAR processing and application, such as target recognition, feature scene classification, speckle noise and ambiguity energy suppression. Next-generation SAR spacecraft may also carry edge computers based on AI platforms. In the future, machine learning SAR will significantly diminish the workload of scientists and engineers, enhance the application value of SAR data, and even influence the underlying design concepts of novel space SAR systems.

Remote Sensing is an established scientific journal attending to the science and application of remote sensing technologies, and encouraging the presentation of unique processing techniques, applied results, and natural findings. As a crucial aspect of remote sensing, SAR provides the microwave scattering characteristics of terrain, but also poses great difficulties in processing and application due to the great variations in human visual habits. The combination of machine learning and SAR can better bridge this gap, again revealing many opportunities and challenges. We hope that this Special Issue will provide an overview of the impact of this technology and exciting results regarding machine learning on SAR remote sensing.

This Special Issue is dedicated to advancing our knowledge in SAR data processing and applications based on machine learning methods. We invite the submission of review and regular papers on machine learning with SAR systems, 2D/3D/4D imaging methods, SAR image enhancement, SAR target detection and terrain classification, SAR image understanding, and trustworthy intelligence processing.

Prof. Dr. Jie Chen
Dr. Peng Xiao
Dr. Yanan You
Dr. Wei Yang
Prof. Dr. John Trinder
Prof. Dr. Dusan Gleich
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
machine learning
trustworthy deep learning
convolutional neural network
SAR imaging
SAR image target recognition
SAR image classification
SAR image understanding

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Published Papers (10 papers)

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21 pages, 7856 KiB

Open AccessArticle

DACLnet: A Dual-Attention-Mechanism CNN-LSTM Network for the Accurate Prediction of Nonlinear InSAR Deformation

by Junyu Lu, Yuedong Wang, Yafei Zhu, Jingtao Liu, Yang Xu, Honglei Yang and Yuebin Wang

Remote Sens. 2024, 16(13), 2474; https://doi.org/10.3390/rs16132474 - 5 Jul 2024

Cited by 7 | Viewed by 1362

Abstract

Nonlinear deformation is a dynamically changing pattern of multiple surface deformations caused by groundwater overexploitation, underground coal mining, landslides, urban construction, etc., which are often accompanied by severe damage to surface structures or lead to major geological disasters; therefore, the high-precision monitoring and prediction of nonlinear surface deformation is significant. Traditional deep learning methods encounter challenges such as long-term dependencies or difficulty capturing complex spatiotemporal patterns when predicting nonlinear deformations. In this study, we developed a dual-attention-mechanism CNN-LSTM network model (DACLnet) to monitor and accurately predict nonlinear surface deformations precisely. Using advanced time series InSAR results as input, the DACLnet integrates the spatial feature extraction capability of a convolutional neural network (CNN), the advantages of the time series learning of a long short-term memory (LSTM) network, and the enhanced focusing effect of the dual-attention mechanism on crucial information, significantly improving the prediction accuracy of nonlinear surface deformations. The groundwater overexploitation area of the Turpan Basin, China, is selected to test the nonlinear deformation prediction effect of the proposed DACLnet. The results demonstrate that the DACLnet accurately captures developmental trends in historical surface deformations and effectively predicts surface deformations for the next two months in the study area. Compared to traditional LSTM and CNN-LSTM methods, the root mean square error (RMSE) of the DACLnet improved by 85.09% and 68.57%, respectively. These research results can provide crucial technical support for the early warning and prevention of geological disasters and can serve as an effective alternative tool for short-term ground subsidence prediction in areas lacking hydrogeological and other related data. Full article

(This article belongs to the Special Issue SAR Data Processing and Applications Based on Machine Learning Method)

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24 pages, 18770 KiB

Open AccessArticle

A Two-Step Phase Compensation-Based Imaging Method for GNSS-Based Bistatic SAR: Extraction and Compensation of Ionospheric Phase Scintillation

by Tao Tang, Pengbo Wang, Jie Chen, Huguang Yao, Ziheng Ren, Peng Zhao and Hongcheng Zeng

Remote Sens. 2024, 16(13), 2345; https://doi.org/10.3390/rs16132345 - 27 Jun 2024

Cited by 1 | Viewed by 1067

Abstract

The GNSS-based bistatic SAR (GNSS-BSAR) system has emerged as a hotspot due to its low power consumption, nice concealment, and worldwide reach. However, the weak landing power density of the GNSS signal often necessitates prolonged integration to achieve an adequate signal-to-noise ratio (SNR). In this case, the effects of the receiver’s time-frequency errors and atmospheric disturbances are significant and cannot be ignored. Therefore, we propose an ionospheric scintillation compensation-based imaging scheme for dual-channel GNSS-BSAR system. This strategy first extracts the reference phase, which contains the ionospheric phase scintillation and other errors. Subsequently, the azimuth phase of the target is divided into difference phase and reference phase. We apply the two-step phase compensation to eliminate Doppler phase errors, thus achieving precise focusing of SAR images. Three sets of experiments using the GPS L5 signal as the illuminator were conducted, coherently processing a 1.5 km by 0.8 km scene about 300 s. The comparative results show that the proposed method exhibited better focusing performance, avoiding the practical challenges encountered by traditional autofocus algorithms. Additionally, ionospheric phase scintillation extracted at different times of the day suggest diurnal variations, preliminary illustrating the potential of this technology for ionospheric-related studies. Full article

(This article belongs to the Special Issue SAR Data Processing and Applications Based on Machine Learning Method)

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24 pages, 12173 KiB

Open AccessArticle

Sea Clutter Suppression Based on Chaotic Prediction Model by Combining the Generator and Long Short-Term Memory Networks

by Jindong Yu, Baojing Pan, Ze Yu, Hongling Zhu, Hanfu Li, Chao Li and Hezhi Sun

Remote Sens. 2024, 16(7), 1260; https://doi.org/10.3390/rs16071260 - 2 Apr 2024

Viewed by 1565

Abstract

Sea clutter usually greatly affects the target detection and identification performance of marine surveillance radars. In order to reduce the impact of sea clutter, a novel sea clutter suppression method based on chaos prediction is proposed in this paper. The method combines a generator trained by Generative Adversarial Networks (GAN) with a Long Short-Term Memory (LSTM) network to accomplish sea clutter prediction. By exploiting the generator’s ability to learn the distribution of unlabeled data, the accuracy of sea clutter prediction is improved compared with the classical LSTM-based model. Furthermore, effective suppression of sea clutter and improvements in the signal-to-clutter ratio of echo were achieved through clutter cancellation. Experimental results on real data demonstrated the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue SAR Data Processing and Applications Based on Machine Learning Method)

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25 pages, 16942 KiB

Open AccessEditor’s ChoiceArticle

TAG-Net: Target Attitude Angle-Guided Network for Ship Detection and Classification in SAR Images

by Dece Pan, Youming Wu, Wei Dai, Tian Miao, Wenchao Zhao, Xin Gao and Xian Sun

Remote Sens. 2024, 16(6), 944; https://doi.org/10.3390/rs16060944 - 7 Mar 2024

Cited by 1 | Viewed by 1611

Abstract

Synthetic aperture radar (SAR) ship detection and classification has gained unprecedented attention due to its important role in maritime transportation. Many deep learning-based detectors and classifiers have been successfully applied and achieved great progress. However, ships in SAR images present discrete and multi-centric features, and their scattering characteristics and edge information are sensitive to variations in target attitude angles (TAAs). These factors pose challenges for existing methods to obtain satisfactory results. To address these challenges, a novel target attitude angle-guided network (TAG-Net) is proposed in this article. The core idea of TAG-Net is to leverage TAA information as guidance and use an adaptive feature-level fusion strategy to dynamically learn more representative features that can handle the target imaging diversity caused by TAA. This is achieved through a TAA-aware feature modulation (TAFM) module. It uses the TAA information and foreground information as prior knowledge and establishes the relationship between the ship scattering characteristics and TAA information. This enables a reduction in the intra-class variability and highlights ship targets. Additionally, considering the different requirements of the detection and classification tasks for the scattering information, we propose a layer-wise attention-based task decoupling detection head (LATD). Unlike general deep learning methods that use shared features for both detection and classification tasks, LATD extracts multi-level features and uses layer attention to achieve feature decoupling and select the most suitable features for each task. Finally, we introduce a novel salient-enhanced feature balance module (SFB) to provide richer semantic information and capture the global context to highlight ships in complex scenes, effectively reducing the impact of background noise. A large-scale ship detection dataset (LSSDD+) is used to verify the effectiveness of TAG-Net, and our method achieves state-of-the-art performance. Full article

(This article belongs to the Special Issue SAR Data Processing and Applications Based on Machine Learning Method)

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20 pages, 7264 KiB

Open AccessArticle

Synthetic Aperture Radar Image Compression Based on Low-Frequency Rejection and Quality Map Guidance

by Jiawen Deng and Lijia Huang

Remote Sens. 2024, 16(5), 891; https://doi.org/10.3390/rs16050891 - 2 Mar 2024

Viewed by 2026

Abstract

Synthetic Aperture Radar (SAR) images are widely utilized in the field of remote sensing. However, there is a limited body of literature specifically addressing the compression of SAR learning images. To address the escalating volume of SAR image data for storage and transmission, which necessitates more effective compression algorithms, this paper proposes a novel framework for compressing SAR images. Experimental validation is performed using a representative low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. Initially, we introduce a novel two-stage transformation-based approach aimed at suppressing the low-frequency components of the input data, thereby achieving a high information entropy and minimizing quantization losses. Subsequently, a quality map guidance image compression algorithm is introduced, involving the fusion of the input SAR images with a target-aware map. This fusion involves convolutional transformations to generate a compact latent representation, effectively exploring redundancies between focused and non-focused areas. To assess the algorithm’s performance, experiments are carried out on both the low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. The results indicate that the low-frequency suppression algorithm significantly outperforms traditional processing algorithms by 3–8 dB when quantifying the input data, effectively preserving image features and improving image performance metrics. Furthermore, the quality map guidance image compression algorithm demonstrates a superior performance compared to the baseline model. Full article

(This article belongs to the Special Issue SAR Data Processing and Applications Based on Machine Learning Method)

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19 pages, 4803 KiB

Open AccessArticle

Pseudo-

L_{0}

-Norm Fast Iterative Shrinkage Algorithm Network: Agile Synthetic Aperture Radar Imaging via Deep Unfolding Network

by Wenjiao Chen, Jiwen Geng, Fanjie Meng and Li Zhang

Remote Sens. 2024, 16(4), 671; https://doi.org/10.3390/rs16040671 - 13 Feb 2024

Viewed by 1268

Abstract

A novel compressive sensing (CS) synthetic-aperture radar (SAR) called AgileSAR has been proposed to increase swath width for sparse scenes while preserving azimuthal resolution. AgileSAR overcomes the limitation of the Nyquist sampling theorem so that it has a small amount of data and low system complexity. However, traditional CS optimization-based algorithms suffer from manual tuning and pre-definition of optimization parameters, and they generally involve high time and computational complexity for AgileSAR imaging. To address these issues, a pseudo-

L_{0}

-norm fast iterative shrinkage algorithm network (pseudo-

L_{0}

-norm FISTA-net) is proposed for AgileSAR imaging via the deep unfolding network in this paper. Firstly, a pseudo-

L_{0}

-norm regularization model is built by taking an approximately fair penalization rule based on Bayesian estimation. Then, we unfold the operation process of FISTA into a data-driven deep network to solve the pseudo-

L_{0}

-norm regularization model. The network’s parameters are automatically learned, and the learned network significantly increases imaging speed, so that it can improve the accuracy and efficiency of AgileSAR imaging. In addition, the nonlinearly sparsifying transform can learn more target details than the traditional sparsifying transform. Finally, the simulated and data experiments demonstrate the superiority and efficiency of the pseudo-

L_{0}

-norm FISTA-net for AgileSAR imaging. Full article

(This article belongs to the Special Issue SAR Data Processing and Applications Based on Machine Learning Method)

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19 pages, 8454 KiB

Open AccessArticle

DeepRED Based Sparse SAR Imaging

by Yao Zhao, Qingsong Liu, He Tian, Bingo Wing-Kuen Ling and Zhe Zhang

Remote Sens. 2024, 16(2), 212; https://doi.org/10.3390/rs16020212 - 5 Jan 2024

Cited by 5 | Viewed by 1727

Abstract

The integration of deep neural networks into sparse synthetic aperture radar (SAR) imaging is explored to enhance SAR imaging performance and reduce the system’s sampling rate. However, the scarcity of training samples and mismatches between the training data and the SAR system pose significant challenges to the method’s further development. In this paper, we propose a novel SAR imaging approach based on deep image prior powered by RED (DeepRED), enabling unsupervised SAR imaging without the need for additional training data. Initially, DeepRED is introduced as the regularization technique within the sparse SAR imaging model. Subsequently, variable splitting and the alternating direction method of multipliers (ADMM) are employed to solve the imaging model, alternately updating the magnitude and phase of the SAR image. Additionally, the SAR echo simulation operator is utilized as an observation model to enhance computational efficiency. Through simulations and real data experiments, we demonstrate that our method maintains imaging quality and system downsampling rate on par with deep-neural-network-based sparse SAR imaging but without the requirement for training data. Full article

(This article belongs to the Special Issue SAR Data Processing and Applications Based on Machine Learning Method)

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28 pages, 17240 KiB

Open AccessArticle

OEGR-DETR: A Novel Detection Transformer Based on Orientation Enhancement and Group Relations for SAR Object Detection

by Yunxiang Feng, Yanan You, Jing Tian and Gang Meng

Remote Sens. 2024, 16(1), 106; https://doi.org/10.3390/rs16010106 - 26 Dec 2023

Cited by 11 | Viewed by 2519

Abstract

Object detection in SAR images has always been a topic of great interest in the field of deep learning. Early works commonly focus on improving performance on convolutional neural network frameworks. More recent works continue this path and introduce the attention mechanisms of Transformers for better semantic interpretation. However, these methods fail to treat the Transformer itself as a detection framework and, therefore, lack the development of various details that contribute to the state-of-the-art performance of Transformers. In this work, we first base our work on a fully multi-scale Transformer-based detection framework, DETR (DEtection TRansformer) to utilize its superior detection performance. Secondly, to acquire rotation-related attributes for better representation of SAR objects, an Orientation Enhancement Module (OEM) is proposed to facilitate the enhancement of rotation characteristics. Then, to enable learning of more effective and discriminative representations of foreground objects and background noises, a contrastive-loss-based GRC Loss is proposed to preserve patterns of both categories. Moreover, to not restrict comparisons exclusively to maritime objects, we have also developed an open-source labeled vehicle dataset. Finally, we evaluate both detection performance and generalization ability on two well-known ship datasets and our vehicle dataset. We demonstrated our method’s superior performance and generalization ability on both datasets. Full article

(This article belongs to the Special Issue SAR Data Processing and Applications Based on Machine Learning Method)

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26 pages, 36367 KiB

Open AccessArticle

Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference

by Jiayi Zhao, Yongliang Wang, Guisheng Liao, Xiaoning Liu, Kun Li, Chunyu Yu, Yang Zhai, Hang Xing and Xuepan Zhang

Remote Sens. 2023, 15(23), 5462; https://doi.org/10.3390/rs15235462 - 22 Nov 2023

Cited by 4 | Viewed by 1605

Abstract

Space-borne synthetic aperture radar (SAR), as an all-weather observation sensor, is an important means in modern information electronic warfare. Since SAR is a broadband active radar system, radio frequency interference (RFI) in the same frequency band will affect the normal observation of the SAR system. Untangling the above problem, this research explores a quick and accurate method for detecting and segmenting RFI-contaminated images. The purpose of the current method is to quickly detect the existence of RFI and to locate it in massive SAR data. Based on deep learning, the method shown in this paper realizes the existence of RFI by determining the presence or absence of interference in the image domain and then performs pixel-level image segmentation on Sentinel-1 RFI-affected quick-look images to locate RFI. Considering the need to quickly detect RFI in massive SAR data, an improved network based on MobileNet is proposed, which replaces some inverted residual blocks in the network with ghost blocks, reducing the number of network parameters and the inference time to 6.1 ms per image. Further, this paper also proposes an improved network called the Smart Interference Segmentation Network (SISNet), which is based on U2Net and replaces the convolution of the VGG blocks in U2Net with a residual convolution and introduces attention mechanisms and a modified RFB module to improve the segmentation mIoU to 87.46% on average. Experiment results and statistical analysis based on the MID dataset and PAIS dataset show that the proposed methods can achieve quicker detection than other CNNs while ensuring a certain accuracy and can significantly improve segmentation performance under the same conditions compared to the original U2Net and other semantic segmentation networks. Full article

(This article belongs to the Special Issue SAR Data Processing and Applications Based on Machine Learning Method)

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20 pages, 13392 KiB

Open AccessTechnical Note

A GAN-Based Augmentation Scheme for SAR Deceptive Jamming Templates with Shadows

by Shinan Lang, Guiqiang Li, Yi Liu, Wei Lu, Qunying Zhang and Kun Chao

Remote Sens. 2023, 15(19), 4756; https://doi.org/10.3390/rs15194756 - 28 Sep 2023

Cited by 2 | Viewed by 1397

Abstract

To realize fast and effective synthetic aperture radar (SAR) deception jamming, a high-quality SAR deception jamming template library can be generated by performing sample augmentation on SAR deception jamming templates. However, the current sample augmentation schemes of SAR deception jamming templates face certain problems. First, the authenticity of the templates is low due to the lack of speckle noise. Second, the generated templates have a low similarity to the target and shadow areas of the input templates. To solve these problems, this study proposed a sample augmentation scheme based on generative adversarial networks, which can generate a high-quality library of SAR deception jamming templates with shadows. The proposed scheme solved the two aforementioned problems from the following aspects. First, the influence of the speckle noise was considered in the network to avoid the problem of reduced authenticity in the generated images. Second, a channel attention mechanism module was used to improve the network’s learning ability of the shadow features, which improved the similarity between the generated template and the shadow area in the input template. Finally, the single generative adversarial network (SinGAN) scheme, which is a generative adversarial network capable of image sample augmentation for a single SAR image, and the proposed scheme were compared regarding the equivalent number of looks and the structural similarity between the target and shadow in the sample augmentation results. The comparison results demonstrated that, compared to the templates generated by the SinGAN scheme, those generated by the proposed scheme had targets and shadow features similar to those of the original image and could incorporate speckle noise characteristics, resulting in a higher authenticity, which helps to achieve fast and effective SAR deception jamming. Full article

(This article belongs to the Special Issue SAR Data Processing and Applications Based on Machine Learning Method)

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