With the development of deep learning, synthetic aperture radar (SAR) ship detection and recognition based on deep learning have gained widespread application and advancement. However, there are still challenging issues, manifesting in two primary facets: firstly, the imaging mechanism of SAR results in significant noise interference, making it difficult to separate background noise from ship target features in complex backgrounds such as ports and urban areas; secondly, the heterogeneous scales of ship target features result in the susceptibility of smaller targets to information loss, rendering them elusive to detection. In this article, we propose a context-aware one-stage ship detection network that exhibits heightened sensitivity to scale variations and robust resistance to noise interference. Then we introduce a Local feature refinement module (LFRM), which utilizes multiple receptive fields of different sizes to extract local multi-scale information, followed by a two-branch channel-wise attention approach to obtain local cross-channel interactions. To minimize the effect of a complex background on the target, we design the global context aggregation module (GCAM) to enhance the feature representation of the target and suppress the interference of noise by acquiring long-range dependencies. Finally, we validate the effectiveness of our method on three publicly available SAR ship detection datasets, SAR-Ship-Dataset, high-resolution SAR images dataset (HRSID), and SAR ship detection dataset (SSDD). The experimental results show that our method is more competitive, with AP50s of 96.3, 93.3, and 96.2% on the three publicly available datasets, respectively.

Ship detection in synthetic aperture radar (SAR) remote sensing imagery is of great significance in military and civilian applications. However, two factors limit detection performance: (1) a high prevalence of small-scale ship targets with limited information content and (2) interference affecting ship detection from speckle noise and land–sea clutter. To address these challenges, we propose a novel end-to-end (E2E) transformer-based SAR ship detection framework, called Flow-Aligned Nested Transformer for SAR Small Ship Detection (FANT-Det). Specifically, in the feature extraction stage, we introduce a Nested Swin Transformer Block (NSTB). The NSTB employs a two-level local self-attention mechanism to enhance fine-grained target representation, thereby enriching features of small ships. For multi-scale feature fusion, we design a Flow-Aligned Depthwise Efficient Channel Attention Network (FADEN). FADEN achieves precise alignment of features across different resolutions via semantic flow and filters background clutter through lightweight channel attention, further enhancing small-target feature quality. Moreover, we propose an Adaptive Multi-scale Contrastive Denoising (AM-CDN) training paradigm. AM-CDN constructs adaptive perturbation thresholds jointly determined by a target scale factor and a clutter factor, generating contrastive denoising samples that better match the physical characteristics of SAR ships. Finally, extensive experiments on three widely used open SAR ship datasets demonstrate that the proposed method achieves superior detection performance, outperforming current state-of-the-art (SOTA) benchmarks.

Sea clutter usually greatly affects the target detection and identification performance of marine surveillance radars. In order to reduce the impact of sea clutter, a novel sea clutter suppression method based on chaos prediction is proposed in this paper. The method combines a generator trained by Generative Adversarial Networks (GAN) with a Long Short-Term Memory (LSTM) network to accomplish sea clutter prediction. By exploiting the generator’s ability to learn the distribution of unlabeled data, the accuracy of sea clutter prediction is improved compared with the classical LSTM-based model. Furthermore, effective suppression of sea clutter and improvements in the signal-to-clutter ratio of echo were achieved through clutter cancellation. Experimental results on real data demonstrated the effectiveness of the proposed method.

Ship detection in optical remote sensing images plays a vital role in numerous civil and military applications, encompassing maritime rescue, port management and sea area surveillance. However, the multi-scale and deformation characteristics of ships in remote sensing images, as well as complex scene interferences such as varying degrees of clouds, obvious shadows, and complex port facilities, pose challenges for ship detection performance. To address these problems, we propose a novel ship detection method by combining multi-scale deformation modeling and fine region highlight-based loss function. First, a visual saliency extraction network based on multiple receptive field and deformable convolution is proposed, which employs multiple receptive fields to mine the difference between the target and the background, and accurately extracts the complete features of the target through deformable convolution, thus improving the ability to distinguish the target from the complex background. Then, a customized loss function for the fine target region highlight is employed, which comprehensively considers the brightness, contrast and structural characteristics of ship targets, thus improving the classification performance in complex scenes with interferences. The experimental results on a high-quality ship dataset indicate that our method realizes state-of-the-art performance compared to eleven considered detection models.

Ship detection in optical remote sensing imagery is pivotal for various civilian and military applications. However, due to the presence of cluttered backgrounds and the significant variations in ship scales, accurately detecting ships in complex maritime environments remains challenging. To address these challenges, we propose the Dual Attention and Scale-aware Feature Alignment Network (DASFA-Net), to effectively detect multi-scale ship targets under clutter interference. The DASFA-Net comprises two primary components: Deformable Spatial Attention Module with Channel Integration (DSAM-CI) and Bidirectional Flow Alignment Network (BiFAN). The DSAM-CI enhances discrimination between ships and distractors by jointly modeling correlations across the channel and spatial domains and applying deformable spatial attention. Concurrently, BiFAN employs a bidirectional feature flow alignment strategy to resolve spatial misalignment in feature fusion, significantly improving the quality of multi-scale feature fusion. Lastly, we design the Dynamic Alpha Complete Intersection over Union (DA-CIoU) based on prior knowledge of target characteristics to explicitly refine our detector for maritime ships. Extensive experiments on three challenging ship detection benchmarks, including HRSC2016, SCCOS, and FGSRCS have demonstrated the superiority of our DASFA.