Small-vessel detection in Synthetic Aperture Radar (SAR) imagery constitutes a critical capability for maritime surveillance systems. However, prevailing methodologies such as sea-clutter statistical models and deep learning-based detectors face three fundamental limitations: weak target scattering signatures, complex sea clutter interference, and computational inefficiency. These challenges create inherent trade-offs between noise suppression and feature preservation while hindering high-resolution representation learning. To address these constraints, we propose the Multi-cue Efficient Maritime detector (MCEM), an anchor-free framework integrating three synergistic components: a Feature Extraction Module (FEM) with scale-adaptive convolutions for enhanced signature representation; a Feature Fusion Module (F²M) decoupling target-background ambiguities; and a Detection Head Module (DHM) optimizing accuracy-efficiency balance. Comprehensive evaluations demonstrate MCEM’s state-of-the-art performance: achieving 45.1% $A{P}_S$ on HRSID (+2.3pp over YOLOv8) and 77.7% $A{P}_L$ on SSDD (+13.9pp over same baseline), the world’s most challenging high-clutter SAR datasets. The framework enables robust maritime surveillance in complex oceanic conditions, particularly excelling in small target detection amidst high clutter.

The application of hyperspectral image (HSI) processing techniques has become increasingly important in many fields such as agriculture, environmental detection, and mining. However, the number of annotated samples in existing hyperspectral datasets is limited, and most hyperspectral classification models typically use patch data for model training. There, the pixels to be classified are often taken as the center, and then a mean pixel length is calculated from the surrounding pixel neighborhood to form patch data. However, during model training, the researchers found that there was an area of overlapping pixels between most of the training data and the test data. This inevitably led to data leakage, resulting in excellent classification performance of the model. To solve this problem, we develop a method of replacing overlapping pixels (ROP) in patch data, which means the training pixel points of the same class are used to replace the test pixel points that appear in the overlapping region of the training patch data. Furthermore, a multiple feature extraction and fusion (MFEF) module is also proposed to enhance the capacity of the HSI model to extract spectral–spatial feature information from new patch data. The results on five publicly available HSI datasets demonstrate that the proposed resolving data leakage network (RDLNet) can provide competitive classification results on the patch data reconstructed with the ROP strategy, which outperforms existing state-of-the-art (SOTA) classification methods as well.

Small ship detection in synthetic aperture radar (SAR) imagery is challenged by speckle noise, strong scale variation, and complex sea clutter, especially under practical efficiency requirements for SAR-based maritime surveillance. We propose SAR-LiteDETR, a lightweight transformer-based detector tailored for maritime SAR scenes. SAR-LiteDETR includes three lightweight plug-and-play modules: PConv-Blocks, an Attention-Guided Feature Pyramid Network (AG-FPN), and a Channel-wise Multi-Branch Fusion Block (CMBF). These three modules improve backbone efficiency and small-target sensitivity, stabilize multi-scale feature alignment under clutter, and adaptively fuse cross-level features to suppress sea clutter and alleviate feature dilution, respectively, while keeping computational cost low. Experiments on the SSDD, HRSID, and LS-SSDD datasets show that SAR-LiteDETR consistently outperforms state-of-the-art and baseline detectors. In particular, it achieves $AP_{S}$ and precision of 69.6%/97.0% on SSDD and 54.2%/93.5% on HRSID, respectively, while requiring only 42.1 GFLOPs. The results indicate that SAR-LiteDETR provides a favorable balance between detection accuracy and efficiency for near real-time SAR-based maritime surveillance.