Multi-Level Intertemporal Attention-Guided Network for Change Detection in Remote Sensing Images

Change detection (CD) is detecting and evaluating surface changes by comparing Remote Sensing Images (RSIs) at different times, which is of great significance for environmental protection and urban planning. Due to the need for higher standards in complex scenes, attention-based CD methods have become predominant. These methods focus on regions of interest, improving detection accuracy and efficiency. However, external factors can introduce many pseudo-changes, presenting significant challenges for CD. To address this issue, we proposed a Multi-level Intertemporal Attention-guided Network (MIANet) for CD. Firstly, an Intertemporal Fusion Attention Unit (IFAU) is proposed to facilitate early feature interaction, which helps eliminate irrelevant changes. Secondly, the Change Location and Recognition Module (CLRM) is designed to explore change areas more deeply, effectively improving the representation of change features. Furthermore, we also employ a challenging landslide mapping dataset for the CD task. Through comprehensive testing on two datasets, the MIANet algorithm proves to be effective and robust, achieving detection results that are either better or at least comparable with current methods in terms of accuracy and reliability.