Abstract
Restricted by technical and budget constraints, hyperspectral images (HSIs) are usually obtained with low spatial resolution. In order to improve the spatial resolution of a given hyperspectral image, a new spatial and spectral image fusion approach via pixel group based non-local sparse representation is proposed, which exploits the spectral sparsity and spectral non-local self-similarity of the hyperspectral image. The proposed approach fuses the hyperspectral image with a high-spatial-resolution multispectral image of the same scene to obtain a hyperspectral image with high spatial and spectral resolutions. The input hyperspectral image is used to train the spectral dictionary, while the sparse codes of the desired HSI are estimated by jointly encoding the similar pixels in each pixel group extracted from the high-spatial-resolution multispectral image. To improve the accuracy of the pixel group based non-local sparse representation, the similar pixels in a pixel group are selected by utilizing both the spectral and spatial information. The performance of the proposed approach is tested on two remote sensing image datasets. Experimental results suggest that the proposed method outperforms a number of sparse representation based fusion techniques, and can preserve the spectral information while recovering the spatial details under large magnification factors.
Highlights
Hyperspectral images (HSIs) usually contain dozens or even hundreds of spectral bands
In contrast to hyperspectral sensors, multispectral sensors produce images with relatively higher spatial resolution but less spectral bands. The fusion of these two types of image data supports the integration of the spatial details of a high spatial resolution multispectral image (MSI) and the spectral information of a HSI, thereby producing a HSI with both high spatial and high spectral resolutions
The proposed pixel group (PG)-NLSR approach fuses an LR-HSI with an HR-MSI of the same scene to improve the spatial resolution of the LR-HSI
Summary
Hyperspectral images (HSIs) usually contain dozens or even hundreds of spectral bands. They are useful for accurate terrain detection, military surveillance and medical diagnosis [1]. In contrast to hyperspectral sensors, multispectral sensors produce images with relatively higher spatial resolution but less spectral bands. The fusion of these two types of image data supports the integration of the spatial details of a high spatial resolution multispectral image (MSI) and the spectral information of a HSI, thereby producing a HSI with both high spatial and high spectral resolutions
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