Change Knowledge-Guided Vision-Language Remote Sensing Change Detection

Remote sensing image change detection plays a critical role in applications like video surveillance and geographic information systems. However, existing binary and semantic change detection (SCD) methods often rely solely on visual information, neglecting language information, which limits interpretability and the ability to provide specific change details. This work proposes the change knowledge-guided vision-language (VL) remote sensing change detection (CKCD) method to address these limitations. By introducing change knowledge as language information, CKCD enhances semantic understanding and change detail representation. A cross-modal affinity (CMA) module is designed to effectively fuse visual and textual features, improving information complementarity and fusion coherence. CKCD further enhances data utilization efficiency by merging change area detection and change category information into a single output through end-to-end learning. This design reduces redundant data representations and simplifies the detection process, leading to a more compact and efficient use of the input data without requiring additional branches or multiple output heads. Experimental results demonstrate consistent performance improvements over traditional methods across multiple change detection datasets.