The ClearSCD model: Comprehensively leveraging semantics and change relationships for semantic change detection in high spatial resolution remote sensing imagery

Abstract

The Earth has been undergoing continuous anthropogenic and natural change. High spatial resolution (HSR) remote sensing imagery provides a unique opportunity to accurately reveal these changes on a planetary scale. Semantic change detection (SCD) with HSR imagery has become a common technique for tracking the evolution of land surface types at a semantic level. However, existing SCD methods rarely model the dependency between semantics and changes, resulting in suboptimal accuracy in detecting complicated surface changes. To address this limitation, we propose ClearSCD, a multi-task learning model that leverages the mutual gain relationship between semantics and change through three innovative modules. The first module interprets semantic features at different times into posterior probabilities for surface types to detect binary change information; the second module learns the correlation between surface types over time and the binary change information; a semantic augmented contrastive learning module is used as the third module to improve the performance of the other two modules. We tested ClearSCD’s performance against state-of-the-art methods on benchmark datasets and a real-world scenario (named LsSCD dataset), showing that ClearSCD outperformed the alternatives on mIoU_sc metrics by 1.23% to 19.34%. Furthermore, ablation experiments demonstrated the unique contribution of the three innovative modules to performance improvement. The high computational efficiency and robust performance over diverse landscapes demonstrate that ClearSCD is an operational tool for detecting detailed land surface changes from HSR imagery. Code and LsSCD dataset available at https://github.com/tangkai-RS/ClearSCD.