Existing SAR ship detectors perform well on data with consistent distributions but degrade significantly when faced with domain shifts and the absence of labeled data. Moreover, traditional CNNs struggle with global feature extraction due to the local receptive fields while transformer approaches struggle with computational efficiency when extracting global features from complex SAR images. Designing an effective cross-domain SAR ship detector that can handle unlabeled data with domain shifts remains a challenge. In this letter, we propose a novel Mamba-based unsupervised domain adaptation SAR ship detection model integrated with pseudo labels optimization strategy. First, we propose the Domain Adaptive State Space Model (DASSM) to construct the Mamba Mean Teacher framework for the first time, enhancing the capture of both global and local SAR image features at a linear time complexity and facilitating domain-invariant feature learning. To enhance the quality of pseudo labels, we design the Adaptive Pseudo Label Optimizer (APLO) module with Wise-IoU (WIoU) and dynamic dual-threshold pseudo label selector (DDPLS). The WIoU is utilized to improve the generation of pseudo labels, while DDPLS is further employed to categorize and optimize pseudo labels. Extensive experiments on public datasets illustrate the effectiveness and superiority of the proposed method for cross-domain detection of unlabeled SAR data.

In synthetic aperture radar (SAR), sidelobes inevitably exist in the matched filtering-based imaging results, which severely affect the image interpretation. Unfortunately, under the near-field condition, the wavenumber spectrum is spatially variant, which means that there is no window function that can effectively weight the wavenumber spectra of all targets at the same time, making sidelobe suppression particularly challenging. To address this issue, an adaptive spatially variant apodization method is proposed in this article. In the proposed method, wavenumber domain weighting is equivalently implemented by a nine-point convolver in the image domain. By adjusting this image domain convolver with pixel position, the method achieves adaptive change of the weighted region, thereby effectively weighting the wavenumber spectra of different targets. In order to avoid the mainlobe broadening caused by weighting, multi-apodization technique is applied to the results after convolution. By transforming multi-apodization into solving a constrained minimization problem, the method achieves the goal of effectively suppressing the sidelobes without reducing the imaging resolution. Experiments on both numerical simulation and measured data verify that the proposed method can suppress the sidelobes by about 10 dB without sacrificing the image resolution.

A lightweight SAR ship detection algorithm is necessary to further meet the demands of military applications. This paper proposes an end-to-end efficient SAR ship target detection algorithm based on RT-DETR called YO-DETR. In the feature extraction network, a CNN-based backbone network is used to replace the transformer-based encoder structure, which retains the original feature extraction capability while reducing the number of parameters. Additionally, in order to retain the characteristic of long-distance feature dependency in transformers, the IRMB module is incorporated into the CNN network to enhance long-distance feature interaction. Finally, the introduction of the decoder head reduces the additional time overhead of traditional NMS during inference. Ultimately, the YO-DETR method achieves a 98.2% mAP on the SSDD dataset with only 5.37M parameters and 10.5M weight size, while the FPS (when batch size is set to 32) also achieves 220.4.

Large-scale, diverse and high-quality data is the foundation and key to achieving good generalization in target detection and recognition for deep learning-based algorithms. Directly collecting synthetic aperture radar (SAR) image data faces the difficulty in acquisition and high costs. Traditional SAR image simulation methods are limited by geometric and electromagnetic computation errors in their modeling process, and the high computational burden as well. Generative adversarial networks (GANs) offer a new approach for SAR image generation, but they struggle to achieve satisfactory results in terms of image quality and diversity. In order to overcome this problem, we propose a new type of GAN to learn the spatial relationship of the targets more effectively. Taking the real SAR images as input, we extract the target information through the capsule network, perturb the extracted features and adopt the attention mechanism to improve the quality and diversity of the augmented data.

In recent years, deep learning has made breakthroughs in the field of computer vision, the single-stage detection algorithm represented by You Only Look Once (YOLO) has achieved satisfying detection results in SAR ship target detection. For the multi-scale problem of SAR ship targets in complex scenes, we proposed an improved YOLOv5 detection method using Convolutional Block Attention Module (CBAM) and Bidirectional Feature Pyramid Network (BiFPN). The CBAM module and BiFPN are added in YOLOv5 so that it can fully learn the feature information of space and channel dimensions, and enhance information fusion transfer between multi-scale targets. Experiments on our dataset show that the proposed YOLOv5 algorithm achieves 92.8% Average Precision (AP), which gains a 1.9% improvement in AP compared to the standard YOLOv5 algorithm in SAR ship target detection. The problem of missed detection of multi-scale targets is well solved.

Current synthetic aperture radar (SAR) ship detectors achieve excellent performance with sufficient samples while encountering degraded results when the sensors and imaging conditions change. The mismatch of view, shape, and illumination inevitably result in the variations of feature distribution between source domain and target domain, which will lead to detection performance degradation. Therefore, devising a detector with well transferability to new domains remains a challenging issue. To this end, this letter proposes a novel domain adaptive YOLOv5 framework for cross-domain SAR ship detection, which is composed of the following keypoints: 1) a cross-domain co- attention feature correlation module, which models spatial and semantic interdependencies by capturing pixel correspondence between source and target domain in a bidirectional way; 2) a multilevel feature alignment module, which constrains the inter-domain difference of features from different scales by inserting three domain classifiers; and 3) teacher–student mutual learning, which makes full use of unlabeled target data and iteratively generates higher-quality pseudo-labels, thus further improving a teacher model with narrowed domain gap. Model performance is evaluated on three SAR ship datasets, and comprehensive results demonstrate the superiority of our method on multiple domain transfer scenarios, i.e., cross resolution, cross-sensor adaptation, and cross-resolution adaptation under the same sensor.