Hyperspectral Image Compressive Sensing Method Based on Reweighted Laplacian Sparse Prior

Abstract

The invention discloses a hyperspectral image compression sensing method based on re-weighted Laplace sparse prior, which is used to solve the technical problem of low reconstruction accuracy of the existing hyperspectral image compression sensing method. The technical solution is to randomly collect a small number of linear observations of each pixel spectrum as compressed data, establish a compressed sensing model based on reweighted Laplace sparse prior and a sparse regularized regression model, and solve the established model. Since a small number of linear observations are randomly collected as compressed data, resource consumption during image acquisition is reduced. The re-weighted Laplacian sparse prior accurately describes the strong sparsity in hyperspectral images, overcomes the non-uniform constraints of traditional Laplacian sparse priors on non-zero elements, and improves the reconstruction accuracy of hyperspectral images. After testing, when the sampling rate is 0.15 and there is strong noise with a signal-to-noise ratio of 10db in the compressed data, the peak signal-to-noise ratio of the present invention is improved by more than 4db compared with the method of the background technology.

Description

Technical field [0001] The present invention relates to a hyperspectral image compressed sensing method, in particular to a hyperspectral image compressed sensing method based on a heavy-weighted Laplacian sparse prior. Background technique [0002] The spectral information of hyperspectral images is helpful for the detection, positioning, classification and identification of remote sensing features. However, the huge data volume of hyperspectral images puts forward strict requirements on the soft and hard resources in image collection, transmission and processing, which restricts hyperspectral images. Image application. Therefore, the hyperspectral image compression algorithm is one of the research hotspots in the hyperspectral field. At present, a large number of common image compression methods have been successfully applied to hyperspectral images. However, this type of method can only compress the acquired images, and cannot reduce the huge resource demand in the imaging p...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the deficiency of low reconstruction accuracy of existing hyperspectral image compression sensing methods, the present invention provides a hyperspectral image compression sensing method based on reweighted Laplacian sparse prior

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