Conjoint Cross-attention Modeling and Joint Feature Calibrating for Remote Sensing Image Change Detection via a Triple-Double Network

Remote sensing (RS) image change detection (CD) based on deep learning (DL), has received increasing attention recently. However, the general independent learning of bi-temporal images ignores the relationship between them, falling short in learning of the change information. In this paper, a Triple-Double (TD) framework with ability of conjoint cross-attention modeling and joint feature calibrating is proposed for CD. Specifically, the TD framework composed of Triple-branch encoder and Double-branch decoder is constructed to extract diverse features and acquire changed maps with the guidance of original edge cues. To enhance the perception of the connection between the bi-temporal features, the multi-scale difference guidance (MDG) module and conjoint cross-attention (CCA) module are designed for the dual-branch encoder, wherein the CCA introduces a novel and efficient rule for modeling the affinity in spatial and channel dimension simultaneously. Furthermore, a joint feature calibration (JFC) module is introduced to enhance the expression of feature diversity in the joint features within the single-branch encoder. Experimental results on three public datasets demonstrate the superiority of the proposed method compared to the state-of-the-art (SOTA) methods.