With the development of remote sensing technology, the resolution of remote sensing images is improving, and the presentation of geomorphic information is becoming more and more abundant, the difficulty of identifying and extracting edge information is also increasing. This paper demonstrates an algorithm to detect the edges of remote sensing images based on Grünwald–Letnikov fractional difference and Otsu threshold. First, a convolution difference mask with two parameters in four directions is constructed by using the definition of the Grünwald–Letnikov fractional derivative. Then, the mask is convolved with the gray image of the remote sensing image, and the edge detection image is obtained by binarization with Otsu threshold. Finally, the influence of two parameters and threshold values on detection results is discussed. Compared with the results of other detectors on the NWPU VHR-10 dataset, it is found that the algorithm not only has good visual effect but also shows good performance in quantitative evaluation indicators (binary graph similarity and edge pixel ratio).

Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention due to its outstanding performance and nonlinear application. However, most existing methods neglect that view-private meaningless information or noise may interfere with the learning of self-expression, which may lead to the degeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistency and Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple views and fuses them based on their discrimination, so that it can effectively explore consistent and complementary information for achieving precise clustering. Specifically, the view-specific self-expression is learned by a self-expression layer embedded into the auto-encoder network for each view. To guarantee consistency across views and reduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastive learning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according to their discrimination. Then they are fused by convolution kernel to obtain consensus self-expression with maximum complementarity of multiple views. Extensive experimental results on four benchmark datasets and one large-scale dataset of the CCAC method outperform other state-of-the-art methods, demonstrating its clustering effectiveness.

Aiming at the problems, such as missed detection of small targets, positioning deviation of rotating targets, and complex background interference in remote sensing images, an improved SSD algorithm based on the High-Level Semantic Information Activation Module (HSIAM) and the improved BSWIoU based on Bhattacharyya distance was proposed. The HSIAM module enhances information fusion capabilities within the deep network. The CA mechanism employs adaptive average pooling to enhance focus on central regions of feature maps, distinguishing small targets within complex backgrounds. The RBD_IoU loss function integrates an orientation-matching constraint and a dynamic weighting mechanism to mitigate rotational bounding box regression bias. Experimental results for three benchmark datasets (DIOR, DOTA, and NWPUCHR) showed that, compared with the baseline SSD algorithm, the mAP50 of the improved model increased by approximately 2%. Furthermore, it achieved a balanced trade-off between accuracy and speed, with 12.5% fewer parameters than YOLOv8s. This provides a high-precision and lightweight solution for target detection in remote sensing images.