Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.

SAR image targets detection and recognition (SAR-TDR) have become research hotpots in the remote sensing application. These targets include ships, vehicles, aircrafts, oil tanks, bridges, and so on. However, with the rapid development of SAR technology and increasingly complex electromagnetic environment, complex characteristics of SAR images bring severe challenges to the accurate SAR-TDR via traditional physical models or manually features extraction based machine learning methods. In recent years, deep learning (DL), as a powerful automatic features extraction algorithm, has been widely used in the computer vision domain. More specifically, DL has also been introduced into the SAR-TDR tasks and effectively achieved good performances in terms of accuracy, real-time, etc. With the rapid development of DL, SAR images processing, and practical requirements of SAR-TDR in civilian and military domains, it is very necessary to make a systematic survey on the SAR-TDR in past few years. In this survey paper, we mainly conduct a systematic overview of DL-based SAR-TDR literatures on two tasks, i.e., targets recognition (e.g., ground vehicles, ships, and aircrafts) and targets detection (e.g., ships, aircrafts,, change detection, seasurface oil spill, and oil tanks), respectively. More specifically, our related works about these topics are also presented to verify the effectiveness of DL-based methods. Firstly, several DL methods (e.g., CNN, RNN, AEs, and GNN), commonly used to SAR-TDR, are briefly introduced. The systematic review of DL-based SAR-TDR (including our related works) is then presented. Finally, the current challenges and future possible research directions are deeply analyzed and discussed.

Synthetic aperture radar (SAR) ship detection based on deep learning has the advantages of high accuracy and end-to-end processing, which has received more and more attention. However, SAR ship detection faces many problems, such as fuzzy ship contour, complex background, large scale difference and dense distribution of small targets. To solve these problems, this paper proposes a SAR ship detection method with ultra lightweight and high detection accuracy based on YOLOX. Aiming at the problem of speckle noise and blurred ship contour caused by the special imaging mechanism of SAR, a SAR ship feature enhancement method based on high frequency sub-band channel fusion which makes full use of contour information is proposed. Aiming at the requirement of light-weight detection algorithms for micro-SAR platforms such as small unmanned aerial vehicle and the defect of spatial pooling pyramid structure damaging ship contour features, an ultra-lightweight and high performance detection backbone based on Ghost Cross Stage Partial (GhostCSP) and lightweight spatial dilation convolution pyramid (LSDP) is designed. Aiming at the characteristics of ship scale diversity and unbalanced distribution of channel feature information after contour enhancement in SAR images, four feature layers are used to fuse contextual semantic information and channel attention mechanism is used for feature enhancement, and finally the improved ship target detection method based on YOLOX (ImYOLOX) is formed. Experimental tests on the SAR Ship Detection Dataset (SSDD) show that the proposed method achieves an average precision of 97.45% with a parameter size of 3.31 MB and a model size of 4.35 MB, and its detection performance is ahead of most current SAR ship detection algorithms.

In this letter, a new optimization algorithm called whale optimization algorithm (WOA) is introduced and applied to the synthesis of uniformly excited broadside linear aperiodic arrays with sidelobe suppression and null steering under constraints on beamwidth requirements. Three design examples of pattern synthesis have been chosen to illustrate the effectiveness and superiority of WOA when compared to synthesis techniques described in literature. The results show that the WOA achieves lowest maximum sidelobe level (MSLL) combined with deepest null depth level (NDL) with narrow first null beamwidth (FNBW) relative to other state-of-the-art algorithms. The WOA results and convergence tests demonstrate superior applicability of this novel optimization algorithm.

Radar signal sorting (RSS) plays an important role in the electronic support measurement system. However, the existing clustering-based RSS methods depend heavily on prior knowledge to achieve excellent performance, which may bring severe challenges to RSS in actual scenarios. This letter proposes a novel subspace decomposition based adaptive density peak clustering (SD-ADPC) method to address the problems of low accuracy and high computational cost in RSS. First, the original complex radar signal data is directly decomposed into two-dimensional (2D) subspace by t-distributed random neighborhood embedding (t-SNE). Then, based on the outlier detection of the products between the peak density and the distance of the data points, ADPC is used to adaptively determine the optimal clustering centers of the original data in 2D subspace. Finally, the reminding data is assigned to its nearest cluster with Euclidean distance in one step. The experimental results of the simulated RSS dataset and the open baselines show that our proposed method does not require any knowledge and can achieve better or competitive performance in terms of accuracy and computational cost, compared to existing state-of-the-art methods.

Deep learning has made rapid progress in the field of synthetic aperture radar (SAR) detection. However, SAR images themselves have limited information, and a general detection network that is too wide and too deep can result in computational complexity and memory waste. Therefore, we design a lightweight network for multi-class SAR detection based on adaptive scale distribution attention. Firstly, a novel backbone is designed from the perspective of lightweight model, using deep separable convolution to generate high-quality feature maps of protruding targets, and applying channel shuffle to improve training and detection efficiency. Secondly, a lightweight adaptive scale distribution attention is proposed, which can adaptively obtain the scattering information of multi-scale targets, aggregate the position and contour features of the targets, and improve the detection accuracy of multi-class targets. Finally, anchor-free detection head is applied to improve the generalization ability and robustness of the model. MLSDNet achieve a high mean average precision (mAP) of 92.99% on the newly released multi-class SAR target datasets (MSAR-1.0) with only 1.42G FLOPs and 928.25K Params. The mAP on SAR ship datasets such as SSDD and HRSID reached 99.1% and 94.7%, respectively. The mAP on the latest SAR aircraft dataset reached 97.7%, demonstrating its good generalization ability. Its performance has reached the state-of-the-art (SOTA).