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

🤖 Plain-English Summary

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

🔑 Key Findings

• In recent years, deep learning (DL)-based methods have made substantial breakthroughs in the field of CD.
• 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.
• To deal with these problems and achieve more accurate results, a deeply supervised (DS) attention metric-based network (DSAMNet) is proposed in this article.

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