Content-Induced Spatial-Spectral Aggregation Network for Change Detection in Remote Sensing Images
Quick Answer
The proposed Content-Guided Spatial-Spectral Integration Network (CSI-Net) enhances change detection in remote sensing images by effectively integrating spatial and spectral information.
Quick Take
The proposed Content-Guided Spatial-Spectral Integration Network (CSI-Net) enhances change detection in remote sensing images by effectively integrating spatial and spectral information. It outperforms state-of-the-art methods on datasets like LEVIR-CD and WHU-CD, demonstrating improved feature learning and reduced spectral differences in unchanged areas.
Key Points
- CSI-Net comprises spatial reasoning, spectral difference, and content-guided integration modules.
- The spatial reasoning module uses cascaded graph convolution blocks for global modeling.
- Spectral features are extracted by calculating means and variances to mitigate differences.
- Experimental results show CSI-Net outperforms existing methods on multiple datasets.
- The model is applicable across various remote sensing scenarios.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 10328v1 Announce Type: new Abstract: The integration of spatial and spectral information is beneficial to the improvement of change detection performance. However, existing methods cannot efficiently suppress the influences of spatial and spectral differences in unchanged areas. To address these issues, in this paper we propose a content-guided spatial-spectral integration network (CSI-Net) for the fusion of global spatial details and spectral difference information.
Specifically, the proposed CSI-Net is composed of a spatial reasoning (SR) module, a spectral difference (SD) module, and a content-guided integration (CGI) module. In the SR module, the spatial information is learned by cascaded graph convolution blocks for global modeling. The SD module is responsible for the extraction of spectral features, by calculating the means and variances of features to reduce the impact of spectral differences in unchanged regions.
In addition, in order to integrate the spatial-spectral features efficiently, we design a CGI module to further take advantage of their complementary information. In this module, high-level content information is introduced as a guide for a proper interaction. Due to the efficient spatial-spectral fusion, the proposed CSI-Net can learn the changed features better while achieving a suppression of spectral differences.
Experimental results on LEVIR-CD, WHU-CD, and CLCD datasets demonstrate that the proposed CSI-Net produces better performance compared to state-of-the-art methods, and is applicable to different scenarios
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