S 3 CD: A Self-Supervised Semantic Change Detection Method by Mining Transition Patterns and Consistency in Remote Sensing Images
提出一种多阶段、多任务、多层次的自监督网络S³CD，通过挖掘双时相遥感图像中的语义一致性和变化模式，在无需大量标注数据的情况下，在语义变化检测和二元变化检测任务上均超越现有监督与自监督方法。
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 S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>CD, 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 S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>CD 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), S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>CD also outperforms most supervised learning methods. Therefore, this approach facilitates the application of self-supervised learning in the field of remote sensing CD.