MCCRNet: A Multi-Level Change Contextual Refinement Network for Remote Sensing Image Change Detection

Abstract

Change detection based on bi-temporal remote sensing images has made significant progress in recent years, aiming to identify the changed and unchanged pixels between a registered pair of images. However, most learning-based change detection methods only utilize fused high-level features from the feature encoder and thus miss the detailed representations that low-level feature pairs contain. Here we propose a multi-level change contextual refinement network (MCCRNet) to strengthen the multi-level change representations of feature pairs. To effectively capture the dependencies of feature pairs while avoiding fusing them, our atrous spatial pyramid cross attention (ASPCA) module introduces a crossed spatial attention module and a crossed channel attention module to emphasize the position importance and channel importance of each feature while simultaneously keeping the scale of input and output the same. This module can be plugged into any feature extraction layer of a Siamese change detection network. Furthermore, we propose a change contextual representations (CCR) module from the perspective of the relationship between the change pixels and the contextual representation, named change region contextual representations. The CCR module aims to correct changed pixels mistakenly predicted as unchanged by a class attention mechanism. Finally, we introduce an effective sample number adaptively weighted loss to solve the class-imbalanced problem of change detection datasets. On the whole, compared with other attention modules that only use fused features from the highest feature pairs, our method can capture the multi-level spatial, channel, and class context of change discrimination information. The experiments are performed with four public change detection datasets of various image resolutions. Compared to state-of-the-art methods, our MCCRNet achieved superior performance on all datasets (i.e., LEVIR, Season-Varying Change Detection Dataset, Google Data GZ, and DSIFN) with improvements of 0.47%, 0.11%, 2.62%, and 3.99%, respectively.