B2CNet: A Progressive Change Boundary-to-Center Refinement Network for Multitemporal Remote Sensing Images Change Detection - Zhiqi Zhang; Liyang Bao; Shao Xiang; Guangqi Xie; Rong Gao (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024)
Change detection is an important method of analyzing information about changes in geographical features. However, existing deep learning feature difference methods often lead to the loss of detailed information. Differences in features can arise from factors like illumination or geometric variations rather than actual change regions, resulting in inaccurate change detection. This leads to poor detection of fine-grained boundaries and internal hole problems. To alleviate this, we propose a novel change detection network guided by change boundary awareness and incorporating the concept of boundary-to-center. Our network introduces a change boundary-aware module to capture boundary information of change regions. This module enhances boundaries, reducing the influence of noise in feature differences and providing rich contextual information to improve the accuracy of change boundaries. Additionally, we propose a bitemporal feature aggregation module (BFAM) based on spatial-temporal features. The BFAM aggregates multiple receptive fields features and complements texture information. Both modules utilize the SimAM attention mechanism to enhance the finegrained nature of the features. In addition, we introduce a deep feature extraction module to extract deep features and minimize information loss during the decoupling process. The proposed change detection network in this article is guided by change boundary perception, progressively integrating semantic and spatial texture information to refine edges and enhance internal integrity. The performance and efficiency of B2CNet have been validated on four publicly available remote sensing image change detection datasets. Through extensive experiments, the effectiveness of the proposed method has been demonstrated. For example, in terms of IOU for LEVIR, WHU, SYSU, and HRCUS datasets, the improvements compared to the baseline are 1.89%, 2.86%, 4.70%, and 3.79%, respectively.