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Target Detection and Classification Based on SAR
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Target Detection and Classification Based on SAR

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

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 2372

Special Issue Editors

Prof. Dr. Gui Gao

E-Mail Website
Guest Editor
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, No. 999 Xi’an Road, Pidu Zone, Chengdu 611756, China
Interests: radar signal processing; SAR target detection; marine environment; SAR GMTI
Special Issues, Collections and Topics in MDPI journals
Dr. Xi Zhang

E-Mail Website
Guest Editor
Laboratory of Marine Physics and Remote Sensing, The First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
Interests: targets detection and classification
Special Issues, Collections and Topics in MDPI journals
Dr. Dingfeng Duan

E-Mail Website
Guest Editor
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, No. 999 Xi'an Road, Pidu Zone, Chengdu 611756, China
Interests: PolSAR classification

Special Issue Information

Dear Colleagues,

Synthetic Aperture Radar (SAR) is one of the most-used tools since it allows all-day and almost all-weather observations with a moderate-to-fine spatial resolution. At its current stage, SAR has the capability to provide very high-resolution images and multi-dimensional (such as multi-channel, multi-aspect, multi-frequency, multi-polarization, multi-temporal, etc.) data, enhancing the spatial-time details of the observations. With the development of machine learning and deep learning methods, the ability to detect and identify target types from SAR data has been greatly improved. However, there is still room to improve both models and methods. The goal of this special issue is to gather high-quality and original contributions that reach beyond the conventional ideas and approaches, and the topics of interest include, but not limited to:

  • Time-sensitivity target detection and identification;
  • Terrain classification and applications;
  • Change detection for classifying man-made targets and nature targets;
  • Synergies between satellite sensors with airborne platforms and multiple satellite SAR;
  • The use of multi-dimensional information to interpret and quantitatively evaluate the target recognition capability;
  • Use of machine learning and the build-up of annotated training databases;
  • Artificial Intelligence for SAR image processing.

Prof. Dr. Gui Gao
Dr. Xi Zhang 
Dr. Dingfeng Duan
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

  • synthetic aperture radar
  • detection
  • classification
  • multi-dimensional information
  • intelligent processing

Published Papers (2 papers)

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Research

25 pages, 13923 KiB  
Article
CCDN-DETR: A Detection Transformer Based on Constrained Contrast Denoising for Multi-Class Synthetic Aperture Radar Object Detection
by Lei Zhang, Jiachun Zheng, Chaopeng Li, Zhiping Xu, Jiawen Yang, Qiuxin Wei and Xinyi Wu
Sensors 2024, 24(6), 1793; https://doi.org/10.3390/s24061793 - 11 Mar 2024
Viewed by 705
Abstract
The effectiveness of the SAR object detection technique based on Convolutional Neural Networks (CNNs) has been widely proven, and it is increasingly used in the recognition of ship targets. Recently, efforts have been made to integrate transformer structures into SAR detectors to achieve [...] Read more.
The effectiveness of the SAR object detection technique based on Convolutional Neural Networks (CNNs) has been widely proven, and it is increasingly used in the recognition of ship targets. Recently, efforts have been made to integrate transformer structures into SAR detectors to achieve improved target localization. However, existing methods rarely design the transformer itself as a detector, failing to fully leverage the long-range modeling advantages of self-attention. Furthermore, there has been limited research into multi-class SAR target detection. To address these limitations, this study proposes a SAR detector named CCDN-DETR, which builds upon the framework of the detection transformer (DETR). To adapt to the multiscale characteristics of SAR data, cross-scale encoders were introduced to facilitate comprehensive information modeling and fusion across different scales. Simultaneously, we optimized the query selection scheme for the input decoder layers, employing IOU loss to assist in initializing object queries more effectively. Additionally, we introduced constrained contrastive denoising training at the decoder layers to enhance the model’s convergence speed and improve the detection of different categories of SAR targets. In the benchmark evaluation on a joint dataset composed of SSDD, HRSID, and SAR-AIRcraft datasets, CCDN-DETR achieves a mean Average Precision (mAP) of 91.9%. Furthermore, it demonstrates significant competitiveness with 83.7% mAP on the multi-class MSAR dataset compared to CNN-based models. Full article
(This article belongs to the Special Issue Target Detection and Classification Based on SAR)
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11 pages, 662 KiB  
Article
SAR Image Generation Method Using DH-GAN for Automatic Target Recognition
by Snyoll Oghim, Youngjae Kim, Hyochoong Bang, Deoksu Lim and Junyoung Ko
Sensors 2024, 24(2), 670; https://doi.org/10.3390/s24020670 - 20 Jan 2024
Viewed by 821
Abstract
In recent years, target recognition technology for synthetic aperture radar (SAR) images has witnessed significant advancements, particularly with the development of convolutional neural networks (CNNs). However, acquiring SAR images requires significant resources, both in terms of time and cost. Moreover, due to the [...] Read more.
In recent years, target recognition technology for synthetic aperture radar (SAR) images has witnessed significant advancements, particularly with the development of convolutional neural networks (CNNs). However, acquiring SAR images requires significant resources, both in terms of time and cost. Moreover, due to the inherent properties of radar sensors, SAR images are often marred by speckle noise, a form of high-frequency noise. To address this issue, we introduce a Generative Adversarial Network (GAN) with a dual discriminator and high-frequency pass filter, named DH-GAN, specifically designed for generating simulated images. DH-GAN produces images that emulate the high-frequency characteristics of real SAR images. Through power spectral density (PSD) analysis and experiments, we demonstrate the validity of the DH-GAN approach. The experimental results show that not only do the SAR image generated using DH-GAN closely resemble the high-frequency component of real SAR images, but the proficiency of CNNs in target recognition, when trained with these simulated images, is also notably enhanced. Full article
(This article belongs to the Special Issue Target Detection and Classification Based on SAR)
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