Synthetic aperture radar (SAR) ship detection faces significant challenges due to complex marine backgrounds, diverse ship scales and shapes, and the demand for lightweight algorithms. Traditional methods, such as constant false alarm rate and edge detection, often underperform in such scenarios. Although deep learning approaches have advanced detection capabilities, they frequently struggle to balance performance and efficiency. Algorithms of the YOLO series offer real-time detection with high efficiency, but their accuracy in intricate SAR environments remains limited. To address these issues, this paper proposes a lightweight SAR ship detection method based on the YOLOv10 framework, optimized across several key modules. The backbone network introduces a StarNet structure with multi-scale convolutional kernels, dilated convolutions, and an ECA module to enhance feature extraction and reduce computational complexity. The neck network utilizes a lightweight C2fGSConv structure, improving multi-scale feature fusion while reducing computation and parameter count. The detection head employs a dual assignment strategy and depthwise separable convolutions to minimize computational overhead. Furthermore, a hybrid loss function combining classification loss, bounding box regression loss, and focal distribution loss is designed to boost detection accuracy and robustness. Experiments on the SSDD and HRSID datasets demonstrate that the proposed method achieves superior performance, with a parameter count of 1.4 million and 5.4 billion FLOPs, and it achieves higher AP and accuracy compared to existing algorithms under various scenarios and scales. Ablation studies confirm the effectiveness of each module, and the results show that the proposed approach surpasses most current methods in both parameter efficiency and detection accuracy.

Superpixel segmentation is a popular preprocessing tool in the field of image processing. Nevertheless, conventional planar superpixel generation algorithms are inadequately suited for segmenting symmetrical spherical images due to the distinctive geometric differences. In this paper, we present a novel superpixel algorithm termed context identity and contour intensity (CICI) that is specifically tailored for spherical scene segmentation. By defining a neighborhood range and regional context identity, we propose a symmetrical spherical seed-sampling method to optimize both the quantity and distribution of seeds, achieving evenly distributed seeds across the panoramic surface. Additionally, we integrate the contour prior to superpixel correlation measurements, which could significantly enhance boundary adherence across different scales. By implementing the two-fold optimizations on the non-iterative clustering framework, we achieve synergistic CICI to generate higher-quality superpixels. Extensive experiments on the public dataset confirm that our work outperforms the baselines and achieves comparable results with state-of-the-art superpixel algorithms in terms of several quantitative metrics.

Recently, hyperspectral image (HSI) mixed denoising methods based on nonlocal subspace representation (NSR) have achieved significant success. However, most of these methods focus on optimizing the denoiser for representation coefficient images (RCIs) without considering how to construct RCIs that better inherit the spatial structure of the clean HSI, thereby affecting subsequent denoising performance. Although existing works have constructed RCIs from the perspective of sparse principal component analysis (SPCA), the refinement of RCIs in mixed noise conditions still leaves much to be desired. To address the aforementioned challenges, in this paper, we reconstructed robust RCIs based on SPCA in mixed noise circumstances to better preserve the spatial structure of the clean HSI. Furthermore, we propose to utilize the robust RCIs as prior information and perform iterative denoising in the denoiser that incorporates low-rank approximation. Extensive experiments conducted on both simulated and real HSI datasets demonstrate that the proposed robust RCIs guidance and low-rank approximation method, denoted as RRGNLA, exhibits competitive performance in terms of mixed denoising accuracy and computational efficiency. For instance, on the Washington DC Mall (WDC) dataset in Case 3, the denoising quantitative metrics of the mean peak signal-to-noise ratio (MPSNR), mean structural similarity index (MSSIM), and spectral angle mean (SAM) are 36.06 dB, 0.963, and 3.449, respectively, with a running time of 35.24 s. On the Pavia University (PaU) dataset in Case 4, the denoising quantitative metrics of MPSNR, MSSIM, and SAM are 34.34 dB, 0.924, and 5.505, respectively, with a running time of 32.79 s.