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Microwave Remote Sensing for Object Detection

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 28689

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Special Issue Editors

Prof. Dr. Guangcai Sun

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Guest Editor

School of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: synthetic apeture radar (SAR) imaging; real-time radar imaging processor; SAR image processing
Special Issues, Collections and Topics in MDPI journals

Dr. Jiang Qian

E-Mail Website
Guest Editor

School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: Synthetic Aperture Radar (SAR) imaging; none-line-of-sight radar imaging

Dr. Lei Yang

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Guest Editor

College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
Interests: Synthetic Apeture Radar (SAR) imaging; moving target imaging; feature enhancement

Dr. Jinsong Zhang

E-Mail Website
Guest Editor

National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
Interests: SAR image processing; target detection and recognition; deep learning in SAR image

Special Issue Information

Dear Colleagues,

As a method of microwave remote sensing, synthetic aperture radar (SAR) technology has developed rapidly in recent years, while the SAR image processing is developing towards achieving higher resolution, multi polarization and high processing speeds. By focusing on various imaging scenes such as airports, harbors, complicated land regions or sea, the SAR images can cover different objects such as airplanes, ships, vehicles, etc. The question of how to locate and find interesting targets quickly and accurately using these large-scale SAR images is clearly gaining significance. For instance, real-time ship detection methods in SAR images are conducive to marine resource management, search and rescue and so on. In particular, the detection and recognition method based on deep learning promotes the ability of target detection in microwave images.

This Special Issue aims to include studies that cover different object detection methods based on different microwave remote sensors and platforms. Topics may cover anything from the target detection, target recognition under complicated land regions or sea conditions, to more comprehensive targets and scenes. Hence, both conventional detection methods and new deep learning-based object detection methods, such as convolutional neural networks and transformer networks for microwave images, are welcome.

Target detection and recognition in microwave images/SAR images;
Deep learning methods for SAR image understanding;
Transfer learning and few sample learning in SAR images.

Prof. Dr. Guangcai Sun
Dr. Jiang Qian
Dr. Lei Yang
Dr. Jinsong Zhang
Guest Editors

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Keywords

Synthetic Aperture Radar (SAR)
airborne and satellite systems
objection detection and recognition
machine learning, compressive sensing
deep neural network sand few sample learning
ground moving target indication (GMTI)
change detection in SAR images
generative adversarial networks (GANs)
ship detection and ship traffic

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Related Special Issue

Microwave Remote Sensing for Object Detection 2nd Edition in Remote Sensing

Published Papers (11 papers)

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17 pages, 21837 KiB

Open AccessArticle

A Lightweight SAR Image Ship Detection Method Based on Improved Convolution and YOLOv7

by Hongdou Tang, Song Gao, Song Li, Pengyu Wang, Jiqiu Liu, Simin Wang and Jiang Qian

Remote Sens. 2024, 16(3), 486; https://doi.org/10.3390/rs16030486 - 26 Jan 2024

Cited by 8 | Viewed by 2145

Abstract

The airborne and satellite-based synthetic aperture radar enables the acquisition of high-resolution SAR oceanographic images in which even the outlines of ships can be identified. The detection of ship targets from SAR images has a wide range of applications. Due to the density of ships in SAR images, the extreme imbalance between foreground and background clutter, and the diversity of target sizes, achieving lightweight and highly accurate multi-scale ship target detection remains a great challenge. To this end, this paper proposed an attention mechanism for multi-scale receptive fields convolution block (AMMRF). AMMRF not only makes full use of the location information of the feature map to accurately capture the regions in the feature map that are useful for detection results, but also effectively captures the relationship between the feature map channels, so as to better learn the relationship between the ship and the background. Based on this, a new YOLOv7-based ship target detection method, You Only Look Once SAR Ship Identification (YOLO-SARSI), was proposed, which acquires the abstract semantic information extracted from the high-level convolution while retaining the detailed semantic information extracted from the low-level convolution. Compared to the deep learning detection methods proposed by previous authors, our method is more lightweight, only 18.43 M. We examined the effectiveness of our method on two SAR image public datasets: the High-Resolution SAR Images Dataset (HRSID) and the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-V1.0). The results show that the average accuracy (

A P_{50}

) of the detection method YOLO-SARSI proposed in this paper on the HRSID and LS-SSDD-V1.0 datasets is 2.6% and 3.9% higher than that of YOLOv7, respectively. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Object Detection)

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19 pages, 6785 KiB

Open AccessArticle

Hardware-Aware Design of Speed-Up Algorithms for Synthetic Aperture Radar Ship Target Detection Networks

by Yue Zhang, Shuai Jiang, Yue Cao, Jiarong Xiao, Chengkun Li, Xuan Zhou and Zhongjun Yu

Remote Sens. 2023, 15(20), 4995; https://doi.org/10.3390/rs15204995 - 17 Oct 2023

Cited by 1 | Viewed by 1491

Abstract

Recently, synthetic aperture radar (SAR) target detection algorithms based on Convolutional Neural Networks (CNN) have received increasing attention. However, the large amount of computation required burdens the real-time detection of SAR ship targets on resource-limited and power-constrained satellite-based platforms. In this paper, we propose a hardware-aware model speed-up method for single-stage SAR ship targets detection tasks, oriented towards the most widely used hardware for neural network computing—Graphic Processing Unit (GPU). We first analyze the process by which the task of detection is executed on GPUs and propose two strategies according to this process. Firstly, in order to speed up the execution of the model on a GPU, we propose SAR-aware model quantification to allow the original model to be stored and computed in a low-precision format. Next, to ensure the loss of accuracy is negligible after the acceleration and compression process, precision-aware scheduling is used to filter out layers that are not suitable for quantification and store and execute them in a high-precision mode. Trained on the dataset HRSID, the effectiveness of this model speed-up algorithm was demonstrated by compressing four different sizes of models (yolov5n, yolov5s, yolov5m, yolov5l). The experimental results show that the detection speeds of yolov5n, yolov5s, yolov5m, and yolov5l can reach 234.7785 fps, 212.8341 fps, 165.6523 fps, and 139.8758 fps on the NVIDIA AGX Xavier development board with negligible loss of accuracy, which is 1.230 times, 1.469 times, 1.955 times, and 2.448 times faster than the original before the use of this method, respectively. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Object Detection)

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27 pages, 7589 KiB

Open AccessArticle

Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering

by Shiqi Huang, Ouya Zhang and Qilong Chen

Remote Sens. 2023, 15(20), 4972; https://doi.org/10.3390/rs15204972 - 15 Oct 2023

Viewed by 1388

Abstract

Ship target detection is an important application of synthetic aperture radar (SAR) imaging remote sensing in ocean monitoring and management. However, SAR imaging is a form of coherence imaging, meaning that there is a large amount of speckle noise in each SAR image. This seriously affects the detection of an SAR image ship target when the fuzzy C-means (FCM) clustering method is used, resulting in numerous errors and incomplete detection. It is also associated with a slow detection speed, which easily falls into the local minima. To overcome these issues, a new method based on block thumbnail particle swarm optimization clustering (BTPSOC) was proposed for SAR image ship target detection. The BTPSOC algorithm uses block thumbnails to segment the main pixels, which improves the resistance to noise interference and segmentation accuracy, enhances the ability to process different types of SAR images, and reduces the runtime. When particle swarm optimization (PSO) technology is used to optimize the FCM clustering center, global optimization is achieved, the clustering performance is improved, the risk of falling into the local minima is overcome, and the stability is improved. The SAR images from two datasets containing ship targets were used in verification experiments. The experimental results show that the BTPSOC algorithm can effectively detect the ship target in SAR images and that it maintains good integrity with regard to the detailed information from the target region. At the same time, experiments comparing the deep convolution neural network (CNN) and constant false alarm rate (CFAR) were conducted. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Object Detection)

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26 pages, 24483 KiB

Open AccessArticle

CViTF-Net: A Convolutional and Visual Transformer Fusion Network for Small Ship Target Detection in Synthetic Aperture Radar Images

by Min Huang, Tianen Liu and Yazhou Chen

Remote Sens. 2023, 15(18), 4373; https://doi.org/10.3390/rs15184373 - 5 Sep 2023

Cited by 2 | Viewed by 1672

Abstract

Detecting small ship targets in large-scale synthetic aperture radar (SAR) images with complex backgrounds is challenging. This difficulty arises due to indistinct visual features and noise interference. To address these issues, we propose a novel two-stage detector, namely a convolutional and visual transformer fusion network (CViTF-Net), and enhance its detection performance through three innovative modules. Firstly, we designed a pyramid structured CViT backbone. This design leverages convolutional blocks to extract low-level and local features, while utilizing transformer blocks to capture inter-object dependencies over larger image regions. As a result, the CViT backbone adeptly integrates local and global information to bolster the feature representation capacity of targets. Subsequently, we proposed the Gaussian prior discrepancy (GPD) assigner. This assigner employs the discrepancy of Gaussian distributions in two dimensions to assess the degree of matching between priors and ground truth values, thus refining the discriminative criteria for positive and negative samples. Lastly, we designed the level synchronized attention mechanism (LSAM). This mechanism simultaneously considers information from multiple layers in region of interest (RoI) feature maps, and adaptively adjusts the weights of diverse regions within the final RoI. As a result, it enhances the capability to capture both target details and contextual information. We achieved the highest comprehensive evaluation results for the public LS-SSDD-v1.0 dataset, with an mAP of 79.7% and an F1 of 80.8%. In addition, the robustness of the CViTF-Net was validated using the public SSDD dataset. Visualization of the experimental results indicated that CViTF-Net can effectively enhance the detection performance for small ship targets in complex scenes. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Object Detection)

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21 pages, 5025 KiB

Open AccessArticle

YOLO-Lite: An Efficient Lightweight Network for SAR Ship Detection

by Xiaozhen Ren, Yanwen Bai, Gang Liu and Ping Zhang

Remote Sens. 2023, 15(15), 3771; https://doi.org/10.3390/rs15153771 - 29 Jul 2023

Cited by 27 | Viewed by 3563

Abstract

Automatic ship detection in SAR images plays an essential role in both military and civilian fields. However, most of the existing deep learning detection methods introduce complex models and huge calculations while improving the detection accuracy, which is not conducive to the application of real-time ship detection. To solve this problem, an efficient lightweight network YOLO-Lite is proposed for SAR ship detection in this paper. First, a lightweight feature enhancement backbone (LFEBNet) is designed to reduce the amount of calculation. Additionally, a channel and position enhancement attention (CPEA) module is constructed and embedded into the backbone network to more accurately locate the target location by capturing the positional information. Second, an enhanced spatial pyramid pooling (EnSPP) module is customized to enhance the expression ability of features and address the position information loss of small SAR ships in high-level features. Third, we construct an effective multi-scale feature fusion network (MFFNet) with two feature fusion channels to obtain feature maps with more position and semantic information. Furthermore, a novel confidence loss function is proposed to effectively improve the SAR ship target detection accuracy. Extensive experiments on SSDD and SAR ship datasets verify the effectiveness of our YOLO-Lite, which can not only accurately detect SAR ships in different backgrounds but can also realize a lightweight architecture with low computation cost. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Object Detection)

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22 pages, 9923 KiB

Open AccessArticle

Scattering-Point-Guided RPN for Oriented Ship Detection in SAR Images

by Yipeng Zhang, Dongdong Lu, Xiaolan Qiu and Fei Li

Remote Sens. 2023, 15(5), 1411; https://doi.org/10.3390/rs15051411 - 2 Mar 2023

Cited by 16 | Viewed by 2831

Abstract

Ship detection in synthetic aperture radar (SAR) images has attracted widespread attention due to its significance and challenges. In recent years, numerous detectors based on deep learning have achieved good performance in the field of SAR ship detection. However, ship targets of the same type always have various representations in SAR images under different imaging conditions, while different types of ships may have a high degree of similarity, which considerably complicates SAR target recognition. Meanwhile, the ship target in the SAR image is also obscured by background and noise. To address these issues, this paper proposes a novel oriented ship detection method in SAR images named SPG-OSD. First, we propose an oriented two-stage detection module based on the scattering characteristics. Second, to reduce false alarms and missing ships, we improve the performance of the network by incorporating SAR scattering characteristics in the first stage of the detector. A scattering-point-guided region proposal network (RPN) is designed to predict possible key scattering points and make the regression and classification stages of RPN increase attention to the vicinity of key scattering points and reduce attention to background and noise. Third, supervised contrastive learning is introduced to alleviate the problem of minute discrepancies among SAR object classes. Region-of-Interest (RoI) contrastive loss is proposed to enhance inter-class distinction and diminish intra-class variance. Extensive experiments are conducted on the SAR ship detection dataset from the Gaofen-3 satellite, and the experimental results demonstrate the effectiveness of SPG-OSD and show that our method achieves state-of-the-art performance. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Object Detection)

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19 pages, 8939 KiB

Open AccessArticle

Real Micro-Doppler Parameters Extraction of Spinning Targets Based on Rotating Interference Antenna

by Zhihao Wang, Yijun Chen, Hang Yuan, Ying Luo and Qun Zhang

Remote Sens. 2022, 14(21), 5300; https://doi.org/10.3390/rs14215300 - 23 Oct 2022

Cited by 7 | Viewed by 1894

Abstract

Micro-Doppler is a unique characteristic of targets with micro-motions, which can provide significant information for target classification and recognition. However, the monostatic radar has the shortcoming of only obtaining the radial micro-motion characteristics. Although the vortex-electromagnetic-wave-based radar has the potential to obtain real micro-motion parameters, it has a high dependence on the mode number and purity of the orbital angular momentum, which greatly restricts its application in the micro-motion parameter extraction. To overcome the above problems, a new radar configuration based on the rotating interference antenna is proposed in this paper. Through the interference processing of the micro-Doppler curves of the rotating and fixed antenna, the curves containing the real micro-motion information of the target can be obtained. Then the real micro-motion characteristics of the spinning target can be reconstructed by the orthogonal matching pursuit algorithm. The effectiveness and robustness of the proposed method are validated by simulations. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Object Detection)

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21 pages, 5539 KiB

Open AccessArticle

YOLO-SD: Small Ship Detection in SAR Images by Multi-Scale Convolution and Feature Transformer Module

by Simin Wang, Song Gao, Lun Zhou, Ruochen Liu, Hengsheng Zhang, Jiaming Liu, Yong Jia and Jiang Qian

Remote Sens. 2022, 14(20), 5268; https://doi.org/10.3390/rs14205268 - 21 Oct 2022

Cited by 25 | Viewed by 3835

Abstract

As an outstanding method for ocean monitoring, synthetic aperture radar (SAR) has received much attention from scholars in recent years. With the rapid advances in the field of SAR technology and image processing, significant progress has also been made in ship detection in SAR images. When dealing with large-scale ships on a wide sea surface, most existing algorithms can achieve great detection results. However, small ships in SAR images contain little feature information. It is difficult to differentiate them from the background clutter, and there is the problem of a low detection rate and high false alarms. To improve the detection accuracy for small ships, we propose an efficient ship detection model based on YOLOX, named YOLO-Ship Detection (YOLO-SD). First, Multi-Scale Convolution (MSC) is proposed to fuse feature information at different scales so as to resolve the problem of unbalanced semantic information in the lower layer and improve the ability of feature extraction. Further, the Feature Transformer Module (FTM) is designed to capture global features and link them to the context for the purpose of optimizing high-layer semantic information and ultimately achieving excellent detection performance. A large number of experiments on the HRSID and LS-SSDD-v1.0 datasets show that YOLO-SD achieves a better detection performance than the baseline YOLOX. Compared with other excellent object detection models, YOLO-SD still has an edge in terms of overall performance. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Object Detection)

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20 pages, 19379 KiB

Open AccessArticle

SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images

by Ping Han, Dayu Liao, Binbin Han and Zheng Cheng

Remote Sens. 2022, 14(18), 4669; https://doi.org/10.3390/rs14184669 - 19 Sep 2022

Cited by 7 | Viewed by 3095

Abstract

Due to the unique imaging mechanism of synthetic aperture radar (SAR), which leads to a discrete state of aircraft targets in images, its detection performance is vulnerable to the influence of complex ground objects. Although existing deep learning detection algorithms show good performance, they generally use a feature pyramid neck design and large backbone network, which reduces the detection efficiency to some extent. To address these problems, we propose a simple and efficient attention network (SEAN) in this paper, which takes YOLOv5s as the baseline. First, we shallow the depth of the backbone network and introduce a structural re-parameterization technique to increase the feature extraction capability of the backbone. Second, the neck architecture is designed by using a residual dilated module (RDM), a low-level semantic enhancement module (LSEM), and a localization attention module (LAM), substantially reducing the number of parameters and computation of the network. The results on the Gaofen-3 aircraft target dataset show that this method achieves 97.7% AP at a speed of 83.3 FPS on a Tesla M60, exceeding YOLOv5s by 1.3% AP and 8.7 FPS with 40.51% of the parameters and 86.25% of the FLOPs. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Object Detection)

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20 pages, 12195 KiB

Open AccessArticle

Micro-Doppler Curves Extraction of Space Target Based on Modified Synchro-Reassigning Transform and Ridge Segment Linking

by Degui Yang, Xing Wang, Jin Li and Zhenghong Peng

Remote Sens. 2022, 14(15), 3691; https://doi.org/10.3390/rs14153691 - 2 Aug 2022

Cited by 2 | Viewed by 1917

Abstract

The micro-movement feature is recognized as one of the practical features of space target recognition in academic circles. The separation of the micro-Doppler curve of the scattering center is the key to feature extraction and parameter estimation, which depends on the time–frequency analysis method. The existing techniques have low separation accuracy and adaptability when there are overlap and noise in the time–frequency domain. This paper proposes a micro-Doppler feature extraction algorithm of a space target based on the modified synchro-reassigning transform (MSRT) and ridge segment linking. The MSRT can eliminate repeated assignment problems, has more accurate micro-Doppler frequency estimates than the synchro-reassigning transform, and has lower computational complexity than second-order synchronous compression and synchronous extraction transforms. The re-linking of the ridge realizes the correct connection of the micro-Doppler curves of each scattering center. The simulation data and the electromagnetic calculation data verify the method’s effectiveness. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Object Detection)

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25 pages, 4406 KiB

Open AccessArticle

A Multi-Pulse Cross Ambiguity Function for the Wideband TDOA and FDOA to Locate an Emitter Passively

by Yuqi Wang, Guang-Cai Sun, Yong Wang, Jun Yang, Zijing Zhang and Mengdao Xing

Remote Sens. 2022, 14(15), 3545; https://doi.org/10.3390/rs14153545 - 24 Jul 2022

Cited by 3 | Viewed by 2519

Abstract

The time difference of arrival (TDOA) and frequency difference of arrival (FDOA) between two receivers are widely used to locate an emitter. Algorithms based on cross ambiguity functions can simultaneously estimate the TDOA and FDOA accurately. However, the algorithms, including the joint processing of received data, require transferring a large volume of data to a central computing unit. It can be a heavy load for the data link, especially for a wideband signal obtained at a high sampling rate. Thus, we proposed a multi-pulse cross ambiguity function (MPCAF) to compress the data before transmitting and then estimate the TDOA and FDOA with the compressed data. The MPCAF consists of two components. First, the raw data are compressed with a proposed two-dimensional compression function. Two methods to construct a reference pulse used in the two-dimensional compression function are considered: a raw data-based method constructs the pulse directly from the received signal, and a signal parameter-based method constructs it through the parameters of the received signal. Second, a wideband cross-correlation function is studied to refine the TDOA and FDOA estimates with the compressed data. The simulation and Cramer–Rao lower bound (CRLB) analyses show that the proposed method dramatically reduces the data transmission load but estimate the TDOA and FDOA well. The hardware-in-the-loop simulation confirms the method’s effectiveness. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Object Detection)

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