S³CD: A Self-Supervised Semantic Change Detection Method by Mining Transition Patterns and Consistency in Remote...

Semantic change detection (SCD) endeavors to identify land-cover changes from multitemporal remote sensing images, providing essential information for various applications. Nevertheless, conventional supervised SCD methods necessitate extensive pixel-level annotations, limiting their applicability. The capability of self-supervised methods to learn feature representations with large amounts of unlabeled data and minimal annotation, and to achieve superior performance, has made them one of the hot topics in remote sensing. However, most self-supervised methods in remote sensing are primarily designed to learn general semantic representations of images, which limits their effectiveness for tasks like SCD that require the analysis of complex semantic transformations. To address this, we propose a multistage, multitask, and multilevel self-supervised network, named S³CD, that learns semantic changes from bi-temporal remote sensing images across scene, pixel, and prototype levels in two stages. In particular, in Stage 2, the network enhances the robustness of SCD by learning semantic consistency within the semantic stable categories across different temporal and capturing the temporal patterns of semantic change categories. We evaluate S³CD on two widely used remote sensing change detection (CD) datasets, where it outperformed state-of-the-art self-supervised and supervised SCD methods. Notably, in the binary CD (BCD) task (i.e., detecting the locations of changes), S³CD also outperforms most supervised learning methods. Therefore, this approach facilitates the application of self-supervised learning in the field of remote sensing CD.