Multi-scale Change-Aware Transformer for Remote Sensing Image Change Detection

Change detection identifies differences between images captured at different times. Real-world change detection faces challenges posed by the diverse and intricate nature of change areas, while current datasets and algorithms are often limited to simpler, consistent changes, reducing their effectiveness in practical applications. Existing dual-branch methods process images independently, risking the loss of change information due to insufficient early interaction. In contrast, single-stream approaches, though improving early integration, lack efficacy in capturing complex changes. To address these limitations, we introduce a novel single-stream framework, the Multi-scale Change-Aware Transformer (MCAT), which features the Dynamic Change-Aware Attention module and the Multi-scale Change-Enhanced Aggregator. The Dynamic Change-Aware Attention module, integrating local self-attention and cross-temporal attention, conducts dynamic iteration on images differences, thereby targeting feature extraction of change areas. The Multi-scale Change-Enhanced Aggregator enables the model to adapt to various scales and complex shapes through local change enhancement and multi-scale aggregation strategies. To overcome the limitations of existing datasets regarding the scale diversity and morphological complexity of change areas, we construct the Mining Area Change Detection dataset. The dataset offers a diverse array of change areas that span multiple scales and exhibit complex shapes, providing a robust benchmark for change detection. Extensive experiments demonstrate that our model outperforms existing methods, especially for irregular and multi-scale changes. Codes and dataset are available at https://github.com/chh11/MCAT.