2023/11/29 by Shi Chen, Lefei Zhang, Chen, Shi +3 · 2 citations
Earth and Planetary Sciences · Engineering · #Advanced Image Fusion Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Remote Sensing and Land Use #Remote-Sensing Image Classification #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2311.17340
openalex publication_date 2023/11/29 · openalex created_date 2023/12/01 · openalex updated_date 2026/07/28
Hyperspectral image super-resolution has attained widespread prominence to enhance the spatial resolution of hyperspectral images. However, convolution-based methods have encountered challenges in harnessing the global spatial-spectral information. The prevailing transformer-based methods have not adequately captured the long-range dependencies in both spectral and spatial dimensions. To alleviate this issue, we propose a novel cross-scope spatial-spectral Transformer (CST) to efficiently investigate long-range spatial and spectral similarities for single hyperspectral image super-resolution. Specifically, we devise cross-attention mechanisms in spatial and spectral dimensions to comprehensively model the long-range spatial-spectral characteristics. By integrating global information into the rectangle-window self-attention, we first design a cross-scope spatial self-attention to facilitate long-range spatial interactions. Then, by leveraging appropriately characteristic spatial-spectral features, we construct a cross-scope spectral self-attention to effectively capture the intrinsic correlations among global spectral bands. Finally, we elaborate a concise feed-forward neural network to enhance the feature representation capacity in the Transformer structure. Extensive experiments over three hyperspectral datasets demonstrate that the proposed CST is superior to other state-of-the-art methods both quantitatively and visually. The code is available at \urlhttps://github.com/Tomchenshi/CST.git.