Deep learning (DL) based synthetic aperture radar (SAR) imagery ship detection is challenged by multiscale ships on the identical SAR image, which inevitably leads to insufficient and low-quality positive samples during training and ultimately degrades detection performance. To address this issue, we propose a Scale-Sensitive Adaptive Sample Allocation Strategy (SSA-SAS) for SAR ship detection. SSA-SAS ranks candidate boxes using a unified score that integrates a scale-sensitive Wasserstein distance (SSWD), a shape cost, and classification confidence. SSWD serves as the core regression metric, enabling adaptive tolerance to positional offsets based on object scale. Meanwhile, the shape cost introduces morphological priors to guide early-stage optimization. These components jointly enhance the quantity and quality of selected positive samples throughout training. Experimental results show that SSA-SAS improves average precision (AP) by up to 2.6% on the high-resolution SAR images dataset for ship detection and instance segmentation (HRSID) dataset and 1.4% on the SAR ship detection dataset (SSDD), while accelerating network convergence by approximately 5.0%.

Detecting ships in Synthetic Aperture Radar (SAR) images is a challenging task due to various factors, such as the diverse distribution of ships and the intricate nature of SAR images. In recent years, deep learning has made excellent progress in the field of SAR interpretation. Models that focus on extracting global semantic information can effectively achieve balanced detection of multi-scale SAR targets, but their computational complexity is relatively high. Models that focus on processing local information have redundant calculations and poor robustness, but are prone to mistaking the background information of SAR images for targets. To address the above issues, we propose a real-time SAR ship detector based on global semantic information enhancement and local gradient information mining. The lightweight feature extraction backbone based on linear computing is designed, with the network structure of Global Information Augmentation Encoder (GIAE)—Local Gradient Information Miner (LGIM)—Decoder, which can quickly perform feature extraction. GIAE enhances the expression of image content through the long sequence modeling capability of the State Space Model. LGIM uses gradient modules composed of depthwise separable convolutions to extract local information of image, and utilizes directed self-attention (DSA) to mine channel context information. GLDet can complete object detection, rotated object detection and instance segmentation tasks by transforming the detection head. Excellent performance has been achieved on the SAR ship instance segmentation dataset SSDD and HRSID, as well as the SAR rotated ship dataset RSDD-SAR and SSDD+. Meanwhile, GLDet demonstrated excellent generalization performance in large-scale SAR images captured by GF-3 and Terra-SAR satellites.

With the rapid development of Synthetic Aperture Radar (SAR), the number and resolution of SAR images are constantly increasing. As a high-value target, aircraft detection has become a research hotspot in the field of SAR image interpretation. SAR aircraft have diverse postures, complex backgrounds, and small differences among different types of aircraft, which can easily lead to false detections. Meanwhile, some SAR aircraft have incomplete structures and are accompanied by speckle noise, which can easily lead to missed detections. To address the above issues, we propose an ultra-lightweight SAR aircraft detection network based on multi-scale matching attention (MSMANET). Firstly, we propose an ultra-lightweight backbone that extracts SAR gradient features through parallel processing of traditional convolution and Ghost modules. Secondly, aiming to the scale, shape and background information of aircraft, Multi-Scale Matching Attention (MSMA) is designed. MSMA performs feature aggregation and cross channel feature matching on multi receptive field feature maps, making the network more focused on feature maps suitable for detection. The mean average precision (mAP) of MSMANET on the SAR-AIRcraft1.0 dataset is as high as 98.4%, with the 1.6 GFLOPS, 657K parameter and 55.1 FPS. Compared to existing advanced networks, the performance has reached SOTA.

Synthetic Aperture Radar (SAR) plays a crucial role in maritime search, rescue operations and port vessel traffic monitoring. Existing algorithms are difficult to simultaneously extract features of multi-scale targets in SAR images, resulting in uneven accuracy in multi-scale ship instance segmentation. Moreover, due to the complexity of image scenes, existing algorithms struggles to accurately segment targets. Regarding the above difficulties, we propose a Pixel level SAR ship instance segmentation network based on global and local feature cross attention (SSGL). We propose a context aware convolutional attention module (CACA). CACA leverages cross-correlation calculations for global information, aiding SSGL in better distinguishing between foreground objects and complex backgrounds. We design a channel optimization module (COM) that combines multi-path convolution with channel attention to adaptively adjust the receptive field, allowing for balanced feature extraction across different target scales. SSGL’s instance segmentation mask achieved 93.6 on the SSDD dataset and 89.1 on the HRSID dataset. Both APMand APLsignificantly surpasses comparative algorithms, proving SSGL's balanced and high-precision instance segmentation for multi-scale targets.

Deep learning has been extensively applied in SAR ship detection because of its powerful feature extraction ability. However, most methods are not only complex, but also easily lead to missed and false detections when the ships are arranged densely. To meet above challenges, a lightweight network LDSS-Net for dense SAR ship detection is proposed. Firstly, we improve the CSP structure and design a lightweight gradient shunt aggregation backbone network LGSA for better feature extraction while reducing computational overhead. Secondly, a feature fusion network DCE-PAN is proposed for enhancing dense ship contour, which enriches the frequency domain information and improves the local feature correlation by using DWT and ECA. Experiments on public datasets SSDD and HRSID demonstrate that the mAP of LDSS-Net reaches 98.20% and 91.29%, respectively, and the parameters are only 2.0M. Our network outperforms existing advanced networks and achieves excellent detection results.