Scene Change Detection by Differential Aggregation Network and Class Probability-Based Fusion Strategy

Scene change detection identifies functional changes at the scene level. Compared with pixel-level and object-level change detection, it can provide a higher level understanding of changes on the Earth’s surface. Triple-branch networks that perform scene binary change detection and scene classification tasks simultaneously are competitive in the field of scene change detection, as they consider both single-temporal scene semantic information and cross-temporal change features. However, some problems still exist. First, the temporal change feature extraction is insufficient, and the 1-D feature vector used for scene change detection and classification is lacking in representativeness. Second, the predicted scene binary change detection and classification results are often contradictory at the network prediction stage, leading to the unsatisfactory performance of change trajectory identification. To address these issues, a novel framework that integrates a differential aggregation network (DAN) and class probability-based fusion strategy (CPFS) was proposed. The designed DAN can fully capture the temporal change features using four advanced differential fusion modules (DFMs) to aggregate the multilevel difference information. In addition, it is able to generate more representative 1-D feature vectors by adopting two novel attention-aware adaptive pooling modules (AAPMs). The developed CPFS produces the final consistent scene binary change detection and classification maps by fusing three predicted class probability vectors. The proposed method was validated on two datasets, and the results demonstrated its superiority to the comparison methods.