关键词: 超分辨率, 高光谱图像, 贝叶斯稀疏表示, 字典学习, 稀疏编码

Abstract:

Aiming at the problem of low spatial resolution of hyperspectral images, this paper analyzes the resolution enhancement methods of hyperspectral images and proposes a super resolution method. The proposed method uses non-parametric Bayesian sparse representation to fuse high-resolution images with low-spatial-resolution hyperspectral images. Firstly, the probability distribution and a set of Bernoulli distribution of material reflectance spectrum in the image are obtained from the hyperspectral image. Secondly, the dictionary is obtained through Bayesian dictionary learning, and the dictionary is transformed according to the spectral quantification of high-resolution image. Thirdly, the transformed dictionary is used to calculate the sparse coding matrix of the high resolution image. Finally, the dictionary and sparse coding matrix are combined to reconstruct the high-resolution hyperspectral image. The experimental results show that the proposed method is superior to the traditional methods in both subjective visual reconstruction of detail information, root mean square error and peak signal to noise ratio. Compared with the similar sparse representation method, the matrix factorization method and the coupled spectral unmixing method, the proposed method has the enhancement of reconstruction effect and its effectiveness is validated.

Key words: super resolution, hyperspectral image, Bayesian sparse representation, dictionary learning, sparse coding