HCGMNET: A Hierarchical Change Guiding Map Network For Change Detection
This work addresses change detection in remote sensing, which is important for applications like urban monitoring, but it appears incremental as it builds on existing hierarchical and attention-based methods.
The paper tackles the challenge of change detection in very-high-resolution remote sensing images by proposing HCGMNet, a hierarchical network that uses multi-scale feature extraction and a change guide module to refine edge features, achieving better performance than existing state-of-the-art methods on two datasets.
Very-high-resolution (VHR) remote sensing (RS) image change detection (CD) has been a challenging task for its very rich spatial information and sample imbalance problem. In this paper, we have proposed a hierarchical change guiding map network (HCGMNet) for change detection. The model uses hierarchical convolution operations to extract multiscale features, continuously merges multi-scale features layer by layer to improve the expression of global and local information, and guides the model to gradually refine edge features and comprehensive performance by a change guide module (CGM), which is a self-attention with changing guide map. Extensive experiments on two CD datasets show that the proposed HCGMNet architecture achieves better CD performance than existing state-of-the-art (SOTA) CD methods.