Axial-Shift Feature Interaction and Prototype-Guided Penalty Constraint for Remote Sensing Change Detection

At present, deep learning (DL) methods for remote sensing (RS) change detection (CD) are developing rapidly. However, there are still many challenges in complex RS scenarios. Factors, such as season and illumination, contribute to minimal differences in radiation characteristics between the changed area and the background, making them difficult to distinguish. This letter proposes an axial-shift feature interaction and prototype-guided penalty constraint network (ASPGNet) to address this problem. ASPGNet integrates axial-shift feature interaction (ASFI) module and prototype-guided penalty constraint (PGPC) loss. The ASFI module facilitates interaction among adjacent features through axial-shift operations in the width/height directions, aiming to obtain discriminative feature representations of the changed area. The PGPC loss utilizes prototypes to adaptively identify and weigh confusing pixel features, ensuring distinguishability between change and nonchange features and, thereby, generating accurate CD results. We evaluate the proposed method on the WHU-CD and LEVIR-CD datasets, achieving the $F1$ scores of 93.22% and 91.49%, respectively. These results demonstrate the effectiveness of the proposed method.