Super-resolution reconstruction method of hyperspectral image based on coupled dictionary and spatial transformation estimation

Abstract

The invention discloses a super-resolution reconstruction method of hyperspectral image based on coupled dictionary and spatial transformation estimation, which is used for solving the existing technical problem of low precision reconstruction of the prior hyperspectral image super-resolution reconstruction method. The hyperspectral image is first linearly unmixed by a spectral unmixing theory. The corresponding spectral dictionary is obtained. The sparse representation theory is used to establish the hyperspectral reconstruction model of hyperspectral image based on coupled dictionary. The spatial transformation matrix between the image and the true color image is a regular term, and reduces the use limit of the algorithm. Then, the improved PALM algorithm is used for solving the model, and the hyperspectral image after super-resolution reconstruction is obtained. Though testing, the results show that the accuracy indexes such as the root-mean-square error RMSE and the spectral angle match SAM are higher than those of the background technology hyperspectral image super-resolution reconstruction method, and have better super-resolution effect when the spatial super-resolution is 32 times.

Description

technical field [0001] The invention relates to a hyperspectral image super-resolution reconstruction method, in particular to a hyperspectral image super-resolution reconstruction method based on coupling dictionary and space transformation estimation. Background technique [0002] The document "Hyperspectral and Multispectral Image Fusion Based on a SparseRepresentation[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(7):3658-3668." discloses a hyperspectral image hyperspectral image based on image fusion and sparse representation. The resolution reconstruction algorithm uses the online learning method to obtain the spectral dictionary of the hyperspectral image, and introduces sparse constraints into the traditional optimization framework, uses the SALSA schema to optimize the solution, and finally obtains the hyperspectral image with high spatial resolution. However, this method does not consider its actual physical meaning when obtaining the dictionary. Th...

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Problems solved by technology

[0003] In order to overcome the deficiency of low reconstruction accuracy of existing hyperspectral image super-resolution reconstruction methods, the present invention provides a hyperspectral image super-resolution reconstruction method based on coupling dictionary and space transformation estimation

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