Hyperspectral Image Compressive Sensing Method Based on Non-Separable Sparse Prior

Abstract

The invention discloses a hyperspectral image compressive sensing method based on nonseparable sparse prior. The hyperspectral image compressive sensing method based on nonseparable sparse prior is used for solving the technical problem that existing hyperspectral image compressive sensing methods are low in reconstruction precision. According to the technical scheme, a few of linear observed values of each pixel spectrum are collected and serve as compressed data, and the resource demand in the image collection process is reduced while substantial data compression is achieved. In the reconstruction process, empirical Bayesian reasoning is utilized to construct nonseparable sparse prior of sparse signals, potential correlation among nonzero elements in the sparse signals is taken into full consideration, and high-precision reconstruction of hyperspectral images is achieved. Because a wavelet orthogonal basis serves as a dictionary according to the method, dependency on end members is eliminated. In addition, through reasoning based on a Bayesian framework, full-automatic estimation of all unknown parameters is achieved, human adjustment is not needed, and adaptability is wide. Experiments show that when the sampling rate is 0.1, the peak signal to noise ratio obtained according to the hyperspectral image compressive sensing method is increased by above 4 db compared with that obtained according to a background technology compressive sensing 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 non-separation sparse prior. Background technique [0002] The hyperspectral image stores the spectral information of the scene in hundreds of bands, which is helpful for the detection, classification and identification of remote sensing objects. However, the rich spectral information leads to a huge amount of hyperspectral image data, and the acquisition, transmission and processing of images consume a lot of resources, which restricts the application of hyperspectral images. Therefore, research on efficient hyperspectral image compression algorithm is one of the hot issues in the hyperspectral field. At present, the classical compression algorithm for ordinary images has been successfully extended to hyperspectral images to simultaneously eliminate redundancy within and between bands in hyperspectral i...

AI Technical Summary

Problems solved by technology

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

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