@inproceedings{
chen2024multiscale,
title={Multi-scale Change-Aware Transformer for Remote Sensing Image Change Detection},
author={HUAN CHEN and Tingfa Xu and Zhenxiang Chen and Peifu Liu and Huiyan Bai and Jianan Li},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=kMQ3LAiWpx}
}
Change detection identifies differences between images captured at different times. Real-world change detection faces challenges from the diverse and intricate nature of change areas, while current datasets and algorithms are often limited to simpler, uniform changes, reducing their effectiveness in practical application. 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 issues, we introduce a novel single-stream architecture, the Multi-scale Change-Aware Transformer (MACT), 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 multiscale 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 the our model outperforms existing methods, especially for irregular and multi-scale changes.
Multi-scale Change-Aware Transformer for Remote Sensing Image Change Detection
HUAN CHEN Tingfa Xu Zhenxiang Chen Peifu Liu Huiyan Bai Jianan Li