Content-Induced Spatial-Spectral Aggregation Network for Change Detection in Remote Sensing Images

The paper introduces the Content-Induced Spatial-Spectral Aggregation Network (CSI-Net) for enhanced change detection in remote sensing images. CSI-Net combines a spatial reasoning module utilizing cascaded graph convolution blocks, a spectral difference module for feature extraction, and a content-guided integration module to effectively fuse spatial and spectral information while mitigating spectral differences in unchanged areas. Experimental results show that CSI-Net outperforms existing state-of-the-art methods on multiple datasets, highlighting its robustness and versatility for practitioners in remote sensing and image analysis.