HCGMNet: A Hierarchical Change Guiding Map Network for Change Detection

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 VHR RS change detection. The model uses h...

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Bibliographic Details
Published inIEEE International Geoscience and Remote Sensing Symposium proceedings pp. 5511 - 5514
Main Authors Han, Chengxi, Wu, Chen, Du, Bo
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.07.2023
Subjects
attention mechanism
Change detection (CD)
Convolution
Data mining
Feature extraction
Image edge detection
Remote sensing
remote sensing (RS) image
Sensors
Task analysis
very-high-resolution (VHR)
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Summary: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 VHR RS change detection. The model uses hierarchical convolution operations to extract multi-scale 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.
ISSN:2153-7003
DOI:10.1109/IGARSS52108.2023.10283341