DiPaSSCD: Difference-Paired Self-Supervised Learning With Object-Based Local Augmentation for Remote Sensing Image Change Detection

Change detection (CD) from remote sensing imagery is an essential task for monitoring various Earth dynamics. Recently, self-supervised learning (SSL) has emerged as a promising paradigm to alleviate the reliance on large-scale annotated datasets. Previous SSL methods for CD suffer from spatial misalignment caused by multi-view augmentation, inherent trade-offs among SSL paradigms, and the lack of explicit change semantics in representation. To overcome these challenges, we propose Difference-Paired Self-Supervised learning for Change Detection (DiPaSSCD), a novel pretraining framework that explicitly integrates change-oriented signals into SSL. The method consists of three key components: (i) Object-Based Local (OBL) Augmentation, which simulates realistic local variations while preserving spatial alignment; (ii) Mask-guided SSL Integration, which jointly optimizes global contrastive and dense prediction objectives to mitigate the limitations of each paradigm; and (iii) Difference-Paired Contrastive Learning (DiPaCL), which directly learns embeddings from paired inputs containing explicit differences, yielding inherently change-related feature representations. Extensive experiments on LEVIR-CD, S2Looking, and DSIFN datasets under both full and label-limited conditions demonstrate that DiPaSSCD consistently outperforms supervised and SSL-based baselines. Qualitative analyses further confirm its ability to suppress pseudo-changes induced by illumination variations while capturing subtle, fine-scale land cover changes. These results highlight the potential of DiPaSSCD as a versatile and architecture-agnostic pretraining paradigm for robust CD across diverse scenarios.