In the domains of geographic information systems and remote sensing image analysis, change detection is vital for examining surface variations in high-resolution remote sensing pictures. However, the intricate texture characteristics and rich details found in high-resolution remote sensing photos are difficult for conventional change detection systems to deal with. Target misdetection, missed detections, and edge blurring are further problems with current deep learning-based methods. This research proposes a high-resolution city change detection network based on difference and attention mechanisms under multi-scale feature fusion (MDANet) to address these issues and improve the accuracy of change detection. First, to extract features from dual-temporal remote sensing pictures, we use the Siamese architecture as the encoder network. The Difference Feature Module (DFM) is employed to learn the difference information between the dual-temporal remote sensing images. Second, the extracted difference features are optimized with the Attention Refinement Module (ARM). The Cross-Scale Fusion Module (CSFM) combines and enhances the optimized attention features, effectively capturing subtle differences in remote sensing images and learning the finer details of change targets. Finally, thorough tests on the BTCDD dataset, LEVIR-CD dataset, and CDD dataset show that the MDANet algorithm performs at a cutting-edge level.

Semantic segmentation of cloud and shadow is an important task in remote sensing and atmospheric science. However, the complexity of cloud/shadow shapes, and noise disturbances (such as snow and ice, buildings, complex backgrounds, and atmospheric optics) make this task challenging. The traditional deep network has good details and generalization due to its local feature extraction ability and spatial invariance, but it is relatively weak in dealing with global context information, which leads to misjudgment and missed judgment in complex scenes. The Transformer can effectively capture long-distance dependencies through the self-attention mechanism, but it may have challenges in extracting local image features and maintaining spatial consistency, resulting in loss of detail information and insufficient generalization. This paper proposes a hybrid branch semantic segmentation network composed of convolutional network and Transformer in parallel. A series of modules are designed to solve the problems of lack of multi-scale feature extraction and insufficient fusion in some of the Convolution-Transformer hybrid networks. In particular, the network utilizes the rich information in auxiliary bands such as near- infrared to improve the segmentation performance, so that the network can process a wider range of data and improve generalization. Experimental results on CloudSEN-12, 38-Cloud, and SPRCS-Val show that our network outperforms existing methods. After introducing the band fusion branch (HyCloudX), the network improves the segmentation performance and generalization, especially in the case of complex noise interference.

In remote sensing image processing, cloud and cloud shadow detection is of great significance, which can solve the problems of cloud occlusion and image distortion, and provide support for multiple fields. However, the traditional convolutional or Transformer models and the existing studies combining the two have some shortcomings, such as insufficient feature fusion, high computational complexity, and difficulty in taking into account local and long-range dependent information extraction. In order to solve these problems, this paper proposes the MCloud model based on Mamba architecture is proposed, which takes advantage of its linear computational complexity to effectively model long-range dependencies and local features through the coordinated work of state space and convolutional support and the Mamba-convolutional fusion module. Experiments show that MCloud have the leading segmentation performance and generalization ability on multiple datasets, and provides more accurate and efficient solutions for cloud and cloud shadow detection.