A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection

Change detection (CD) aims to identify surface changes from bitemporal images. Experiments are conducted on both the CDD and the SYSU-CD dataset.

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• 1 In recent years, deep learning (DL)-based methods have made substantial breakthroughs in the field of CD.
• 2 However, CD results can be easily affected by external factors, including illumination, noise, and scale, which leads to pseudo-changes and noise in the detection map.
• 3 To deal with these problems and achieve more accurate results, a deeply supervised (DS) attention metric-based network (DSAMNet) is proposed in this article.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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