Hyperspectral image compressed sensing method based on heavy weighting laplacian sparse prior

Abstract

The invention discloses a hyperspectral image compressed sensing method based on the heavy weighting laplacian sparse prior. The hyperspectral image compressed sensing method based on the heavy weighting laplacian sparse prior is used for solving the technical problem that an existing hyperspectral image compressed sensing method is low in reconstruction accuracy. According to the technical scheme, a little linear observation of each pixel spectrum is collected randomly as compressed data, a compressed sensing model based on the heavy weighting laplacian sparse prior and a sparse regulated regression model is established, and solving on the established models is conducted. According to the hyperspectral image compressed sensing method based on the heavy weighting laplacian sparse prior, and a little linear observation of each pixel spectrum is collected randomly as compressed data, so that resource consumption in the image collecting process is reduced; the strong sparsity of the hyperspectral image is depicted accurately through the heavy weighting laplacian sparse prior, inhomogeneous constraint on the nonzero element of the traditional laplacian sparse prior is overcome, and the reconstruction accuracy of the hyperspectral image is improved. It is tested that when a sampling rate is 0.15 and the compressed data consist strong noise with 10 db signal-to-noise ratio, and the peak signal-to-noise ratio promotes over 4 db relative to a background technology method.

Description

technical field [0001] The invention relates to a hyperspectral image compression sensing method, in particular to a hyperspectral image compression sensing method based on reweighted Laplace sparse prior. Background technique [0002] The spectral information of hyperspectral images is helpful for the detection, location and classification of remote sensing ground objects. However, the huge amount of data in hyperspectral images imposes strict requirements on the soft and hard resources in image acquisition, transmission and processing, which restricts the hyperspectral Image application. Therefore, 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, such methods can only compress the already acquired images and cannot reduce the huge resource requirements in the imaging process. In recent years,...

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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