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Target Detection and Information Extraction in Radar Images

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

Deadline for manuscript submissions: closed (1 July 2022) | Viewed by 30703

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Special Issue Editors

Dr. M. Pilar Jarabo Amores

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Guest Editor

Department of Signal Theory and Communications, University of Alcala, Alcala de Henares, Spain
Interests: statistical signal processing; artificial intelligence; signal models; radar signal processing techniques; antenna and RF chains design; advanced digital communications systems; EMC-EMI

Dr. David de la Mata Moya

E-Mail Website
Guest Editor

Department of Signal Theory and Communications, University of Alcala, Alcala de Henares, Spain
Interests: statistical signal processing; artificial intelligence systems; signal models; radar signal processing; radar systems analysis and design

Special Issue Information

Dear Colleagues,

Radar signal processing techniques for detection and classification are the focus of intense research due to the continuous evolution of threats like unmanned systems. Border security, traffic management (air, maritime, and ground), and the security for critical infrastructures and smart cities, including public spaces, are applications that demand new solutions.

Radars must face the detection of small low reflectivity targets (aerial and maritime drones, inflatable or wood boats, ultralight aircraft, etc.), in complex environments (rural and coastal with complex relief, semi-urban and urban with big buildings, urban furniture, etc.), being the possibility of designing and integrating smart distributed sensor networks in the application scenario of great interest. Low cost and low probability of intercept (LPI) solutions can be key elements in these sensor networks for reducing reconfiguration and updating cost and for avoiding potential electromagnetic interferences, reducing the possibility of being intercepted and counter-measured. On the other hand, the possibility of threat classification and recognition is the desired result to evaluate the risk associated with the detected object in the considered complex environments.

This Special Issue will collect work on the most recent advances in radar detection and classification techniques. Contributions can tackle topics ranging from the signal processing carried out by a specific sensor node to the fusion of data provided by a set of nodes in a distributed sensor network, including (but not limited to) the following:

Target RCS and clutter modeling at frequencies used by active radars and opportunity illuminators used by passive ones.

Wideband signal processing for radar detection and classification, including sparse frequency signals and the use of compressive sensing techniques.

Neyman–Pearson approximation based on statistical signal models, including the use of intelligent agents.

Smart distributed sensor networks: sensor distribution and data fusion for detection, tracking, and imaging.

Novel classification and recognition techniques, at tracking and radar imaging levels.

Dr. M. Pilar Jarabo Amores
Dr. David de la Mata Moya
Guest Editors

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Keywords

Active/passive radar target and clutter statistical modeling
Smart distributed sensor network
Wideband signal processing
Sparsity and compressive sensing
Neyman–Pearson approximation
Data fusion for detection and classification
Intelligent agents for detection and classification

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Published Papers (10 papers)

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Research

20 pages, 9869 KiB

Open AccessArticle

Motion Compensation for Long Integration Times and DoA Processing in Passive Radars

by Anabel Almodóvar-Hernández, David Mata-Moya, María-Pilar Jarabo-Amores, Nerea Rey-Maestre and María Benito-Ortiz

Remote Sens. 2023, 15(4), 1031; https://doi.org/10.3390/rs15041031 - 13 Feb 2023

Cited by 7 | Viewed by 2491

Abstract

In this work, a multistage target motion compensation solution for long integration times and the direction of arrival processing in geostationary-satellite-based passive radars is presented. Long integration processing intervals are considered to compensate for the associated propagation loss, but during this time target dynamics can extend the backscattering in more than one range or Doppler cell. To control the gain-processing reduction, a combination of detection, tracking, feature extraction, and filtering techniques is designed to provide automatic adaptation to each unknown target dynamic in the area of interest. The proposed methodology is validated with real data acquired by the passive radar demonstrator developed by the University of Alcalá (IDEPAR), and the results confirm that target monitoring exploiting digital video broadcasting-satellite (DVB-S) signals is clearly improved. Full article

(This article belongs to the Special Issue Target Detection and Information Extraction in Radar Images)

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13 pages, 2998 KiB

Open AccessCommunication

Adaptive Subspace Signal Detection in Structured Interference Plus Compound Gaussian Sea Clutter

by Zeyu Wang, Jun Liu, Yachao Li, Hongmeng Chen and Mugen Peng

Remote Sens. 2022, 14(9), 2274; https://doi.org/10.3390/rs14092274 - 8 May 2022

Cited by 6 | Viewed by 2042

Abstract

This paper discusses the problem of detecting subspace signals in structured interference plus compound Gaussian sea clutter with persymmetric structure. The sea clutter is represented by a compound Gaussian process wherein the texture obeys the inverse Gaussian distribution. The structured interference lies in a known subspace, which is independent with the target signal subspace. By resorting to the two-step generalized likelihood ratio test, two-step Rao, and two-step Wald design criteria, three adaptive subspace signal detectors are proposed. Moreover, the constant false-alarm rate property of the proposed detectors is proved. The experimental results based on IPIX real sea clutter data and simulated data illustrate that the proposed detectors outperform their counterparts. Full article

(This article belongs to the Special Issue Target Detection and Information Extraction in Radar Images)

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18 pages, 11193 KiB

Open AccessArticle

Ship Detection in SAR Images Based on Multi-Scale Feature Extraction and Adaptive Feature Fusion

by Kexue Zhou, Min Zhang, Hai Wang and Jinlin Tan

Remote Sens. 2022, 14(3), 755; https://doi.org/10.3390/rs14030755 - 6 Feb 2022

Cited by 57 | Viewed by 6591

Abstract

Deep learning has attracted increasing attention across a number of disciplines in recent years. In the field of remote sensing, ship detection based on deep learning for synthetic aperture radar (SAR) imagery is replacing traditional methods as a mainstream research method. The multiple scales of ship objects make the detection of ship targets a challenging task in SAR images. This paper proposes a new methodology for better detection of multi-scale ship objects in SAR images, which is based on YOLOv5 with a small model size (YOLOv5s), namely the multi-scale ship detection network (MSSDNet). We construct two modules in MSSDNet: the CSPMRes2 (Cross Stage Partial network with Modified Res2Net) module for improving feature representation capability and the FC-FPN (Feature Pyramid Network with Fusion Coefficients) module for fusing feature maps adaptively. Firstly, the CSPMRes2 module introduces modified Res2Net (MRes2) with a coordinate attention module (CAM) for multi-scale features extraction in scale dimension, then the CSPMRes2 module will be used as a basic module in the depth dimension of the MSSDNet backbone. Thus, our backbone of MSSDNet has the capabilities of features extraction in both depth and scale dimensions. In the FC-FPN module, we set a learnable fusion coefficient for each feature map participating in fusion, which helps the FC-FPN module choose the best features to fuse for multi-scale objects detection tasks. After the feature fusion, we pass the output through the CSPMRes2 module for better feature representation. The performance evaluation for this study is conducted using an RTX2080Ti GPU, and two different datasets: SSDD and SARShip are used. These experiments on SSDD and SARShip datasets confirm that MSSDNet leads to superior multi-scale ship detection compared with the state-of-the-art methods. Moreover, in comparisons of network model size and inference time, our MSSDNet also has huge advantages with related methods. Full article

(This article belongs to the Special Issue Target Detection and Information Extraction in Radar Images)

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19 pages, 12631 KiB

Open AccessArticle

PolSAR Cell Information Representation by a Pair of Elementary Scatterers

by Konstantinos Karachristos, Georgia Koukiou and Vassilis Anastassopoulos

Remote Sens. 2022, 14(3), 695; https://doi.org/10.3390/rs14030695 - 1 Feb 2022

Cited by 6 | Viewed by 1911

Abstract

This study exploits Cameron’s decomposition for polarimetric data analysis and presents an information extraction process so that each PolSAR cell (pixel) is interpreted by two dominating elementary scattering mechanisms each one contributing to the scattering behavior of the SAR pixel with its own weight. The co-distance matrix is introduced to depict the metric distances between these two nearest scattering mechanisms. For most of the scattering mechanisms in each resolution cell, the strength between the first and the second nearest elementary scatterer usually differs slightly. This indicates that the interpretation of the available information in a PolSAR pixel by a single dominant scatterer, as most methods employ, is not adequate. The proposed method presents an alternative to Cameron’s spherical topology by taking advantage of the elementary scattering mechanisms complementary nature and inspired by the philosophy of principal component analysis. According to the introduced topology four elementary scatterers, which are in pairs complementary to each other, are adequate to characterize each PolSAR pixel. The aim of this research is to present a new feature-toοl with a more stochastic nature that could fit a variety of techniques that utilize fully polarimetric data. To prove the strength of the proposed method, the double-scatterer model is applied for interpreting each pixel on a variety of land cover types presenting a richer feature extraction capability, effective in detection and classification procedures. Full article

(This article belongs to the Special Issue Target Detection and Information Extraction in Radar Images)

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12 pages, 8817 KiB

Open AccessCommunication

An Efficient Maritime Target Joint Detection and Imaging Method with Airborne ISAR System

by Haodong Li, Guisheng Liao, Jingwei Xu and Lan Lan

Remote Sens. 2022, 14(1), 193; https://doi.org/10.3390/rs14010193 - 1 Jan 2022

Cited by 5 | Viewed by 1771

Abstract

In this paper, a joint maritime moving target detection and imaging approach, referred to as the fast inverse synthetic aperture radar (ISAR) imaging approach, based on the multi-resolution space−time adaptive processing (STAP), is proposed to improve the target detection performance and the target imaging efficiency in an airborne radar system. In the target detection stage, the sub-band STAP is introduced to improve the robustness of clutter suppression and to enhance the target output power with the decreased range resolution, by which the coarse estimation of target range-Doppler (R-D) location is obtained as the prior knowledge. In the following target imaging stage, the ISAR imaging is applied in the localized R-D zone surrounding with the target location. However, it is difficult to directly apply ISAR imaging with the conventional R-D algorithm because the slow-moving maritime target cannot be separated from the clutter interference in the Doppler frequency dimension. In this regard, the full-band STAP is applied in the R-D two-dimensional frequency domain for the simultaneous clutter suppression and high-resolution ISAR imaging, in which the envelope alignment and phase compensation are achieved by adaptive match filtering with the target Doppler frequency coarse estimation. Moreover, the reduced-dimension STAP applied in the target-surrounded localized Doppler frequency zone gives facilities for alleviating the computation burden. Simulation results corroborate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Target Detection and Information Extraction in Radar Images)

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22 pages, 5013 KiB

Open AccessArticle

Frequency Diversity Gain of a Wideband Radar Signal

by Mengmeng Shen, Feng He, Zhen Dong, Xing Chen, Lei Yu and Manqing Wu

Remote Sens. 2021, 13(23), 4885; https://doi.org/10.3390/rs13234885 - 1 Dec 2021

Cited by 3 | Viewed by 1911

Abstract

Wideband radar has high-range directional resolution, which can effectively reduce the fluctuation of echo and improve the detection probability of a target under the same detection probability requirement. In this paper, a unified wideband radar χ² distribution target model with more practical significance is innovatively established, on which the probability density function and detection probability function of Swerling 0, Swerling II and Swerling IV targets are analyzed, respectively. A generalized “frequency diversity gain” of wideband radar is proposed and defined based on the contradiction between suppression of fluctuation and accumulation loss, which represents the ratio of Signal-to-Noise Ratio (SNR) gain between broadband signal and reference bandwidth signal under the same condition (when the reference bandwidth is used, the radar target has only one range unit), and the mathematical relation equation of the target detection performance and signal bandwidth (equivalent to the number of distinguishable range elements of the target) is given. A Monte Carlo simulation experiment is designed. Based on the target model established in this paper, the optimal number of target range units corresponding to different detection probability requirements is obtained, which verifies the correctness of the concept proposed in this paper. Full article

(This article belongs to the Special Issue Target Detection and Information Extraction in Radar Images)

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27 pages, 6685 KiB

Open AccessArticle

Adversarial Self-Supervised Learning for Robust SAR Target Recognition

by Yanjie Xu, Hao Sun, Jin Chen, Lin Lei, Kefeng Ji and Gangyao Kuang

Remote Sens. 2021, 13(20), 4158; https://doi.org/10.3390/rs13204158 - 17 Oct 2021

Cited by 23 | Viewed by 3210

Abstract

Synthetic aperture radar (SAR) can perform observations at all times and has been widely used in the military field. Deep neural network (DNN)-based SAR target recognition models have achieved great success in recent years. Yet, the adversarial robustness of these models has received far less academic attention in the remote sensing community. In this article, we first present a comprehensive adversarial robustness evaluation framework for DNN-based SAR target recognition. Both data-oriented metrics and model-oriented metrics have been used to fully assess the recognition performance under adversarial scenarios. Adversarial training is currently one of the most successful methods to improve the adversarial robustness of DNN models. However, it requires class labels to generate adversarial attacks and suffers significant accuracy dropping on testing data. To address these problems, we introduced adversarial self-supervised learning into SAR target recognition for the first time and proposed a novel unsupervised adversarial contrastive learning-based defense method. Specifically, we utilize a contrastive learning framework to train a robust DNN with unlabeled data, which aims to maximize the similarity of representations between a random augmentation of a SAR image and its unsupervised adversarial example. Extensive experiments on two SAR image datasets demonstrate that defenses based on adversarial self-supervised learning can obtain comparable robust accuracy over state-of-the-art supervised adversarial learning methods. Full article

(This article belongs to the Special Issue Target Detection and Information Extraction in Radar Images)

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18 pages, 4783 KiB

Open AccessArticle

Ship Object Detection of Remote Sensing Image Based on Visual Attention

by Yuxin Dong, Fukun Chen, Shuang Han and Hao Liu

Remote Sens. 2021, 13(16), 3192; https://doi.org/10.3390/rs13163192 - 12 Aug 2021

Cited by 24 | Viewed by 3679

Abstract

At present, reliable and precise ship detection in high-resolution optical remote sensing images affected by wave clutter, thin clouds, and islands under complex sea conditions is still challenging. At the same time, object detection algorithms in satellite remote sensing images are challenged by color, aspect ratio, complex background, and angle variability. Even the results obtained based on the latest convolutional neural network (CNN) method are not satisfactory. In order to obtain more accurate ship detection results, this paper proposes a remote sensing image ship object detection method based on a brainlike visual attention mechanism. We refer to the robust expression mode of the human brain, design a vector field filter with active rotation capability, and explicitly encode the direction information of the remote sensing object in the neural network. The progressive enhancement learning model guided by the visual attention mechanism is used to dynamically solve the problem, and the object can be discovered and detected through time–space information. To verify the effectiveness of the proposed method, a remote sensing ship object detection data set is established, and the proposed method is compared with other state-of-the-art methods on the established data set. Experiments show that the object detection accuracy of this method and the ability to capture image details have been improved. Compared with other models, the average intersection rate of the joint is 80.12%, which shows a clear advantage. The proposed method is fast enough to meet the needs of ship detection in remote sensing images. Full article

(This article belongs to the Special Issue Target Detection and Information Extraction in Radar Images)

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20 pages, 464 KiB

Open AccessArticle

Passive MIMO Radar Detection with Unknown Colored Gaussian Noise

by Yongjun Liu, Guisheng Liao, Haichuan Li, Shengqi Zhu, Yachao Li and Yingzeng Yin

Remote Sens. 2021, 13(14), 2708; https://doi.org/10.3390/rs13142708 - 9 Jul 2021

Cited by 5 | Viewed by 2225

Abstract

The target detection of the passive multiple-input multiple-output (MIMO) radar that is comprised of multiple illuminators of opportunity and multiple receivers is investigated in this paper. In the passive MIMO radar, the transmitted signals of illuminators of opportunity are totally unknown, and the received signals are contaminated by the colored Gaussian noise with an unknown covariance matrix. The generalized likelihood ratio test (GLRT) is explored for the passive MIMO radar when the channel coefficients are also unknown, and the closed-form GLRT is derived. Compared with the GLRT with unknown transmitted signals and channel coefficients but a known covariance matrix, the proposed method is applicable for a more practical case whenthe covariance matrix of colored noise is unknown, although it has higher computational complexity. Moreover, the proposed GLRT can achieve similar performance as the GLRT with the known covariance matrix when the number of training samples is large enough. Finally, the effectiveness of the proposed GLRT is verified by several numerical examples. Full article

(This article belongs to the Special Issue Target Detection and Information Extraction in Radar Images)

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21 pages, 14551 KiB

Open AccessArticle

A Deep Vector Quantization Clustering Method for Polarimetric SAR Images

by Yixin Zuo, Jiayi Guo, Yueting Zhang, Bin Lei, Yuxin Hu and Mingzhi Wang

Remote Sens. 2021, 13(11), 2127; https://doi.org/10.3390/rs13112127 - 28 May 2021

Cited by 8 | Viewed by 3135

Abstract

Convolutional Neural Network (CNN) models are widely used in supervised Polarimetric Synthetic Aperture Radar (PolSAR) image classification. They are powerful tools to capture the non-linear dependency between adjacent pixels and outperform traditional methods on various benchmarks. On the contrary, research works investigating unsupervised PolSAR classification are quite rare, because most CNN models need to be trained with labeled data. In this paper, we propose a completely unsupervised model by fusing the Convolutional Autoencoder (CAE) with Vector Quantization (VQ). An auxiliary Gaussian smoothing loss is adopted for better semantic consistency in the output classification map. Qualitative and quantitative experiments are carried out on satellite and airborne full polarization data (RadarSat2/E-SAR, AIRSAR). The proposed model achieves 91.87%, 83.58% and 96.93% overall accuracy (OA) on the three datasets, which are much higher than the traditional H/alpha-Wishart method, and it exhibits better visual quality as well. Full article

(This article belongs to the Special Issue Target Detection and Information Extraction in Radar Images)

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