Hyperspectral Image Superresolution via Structure-Tensor-Based Image Matting

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 14; pp. 7994 - 8007
• Main Authors: Gao, Han, Zhang, Guifeng, Huang, Min
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computer vision; Distortion; Hyperspectral (HS) image; Hyperspectral imaging; Image enhancement; image matting; Image processing; Image quality; Image reconstruction; Image resolution; Imaging; Mathematical analysis; Object recognition; Pattern recognition; Procedures; Remote sensing; Resolution; Spatial discrimination; Spatial resolution; Spectra; Spectral signatures; structure tensor; Superresolution; Tensors
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2021.3102579

Hyperspectral (HS) imaging has achieved breakthroughs in many applications, such as remote sensing and object recognition. However, the spatial resolution of HS images is still insufficient due to the limitations of sensor technology and cost. In this article, we propose an HS image superresolution method that combines low-resolution (LR) HS images and high-resolution (HR) panchromatic (PAN) images. To exploit the spectral signatures in the LR-HS images while introducing details from the HR-PAN images during the image fusion procedure, an image matting model is used to fuse the original LR-HS images and the HR-PAN images. Specifically, to preserve the spectral components during the fusion procedure, two different alpha channels in the image matting model are generated based on the HS and PAN image structure tensors, which suppress spectral distortion and improve the quality of the reconstructed HR-HS image. Experimental results based on public datasets demonstrate the advantage of our proposed method in both preserving spectral information and enhancing HS image spatial resolution.