Hyperspectral unmixing compressive sensing method based on three-dimensional total variation sparse prior

Abstract

The invention discloses a hyperspectral unmixing compressive sensing method based on three-dimensional total variation sparse prior. The hyperspectral unmixing compressive sensing method is used for solving the technical problem that an existing hyperspectral image compressive sensing algorithm in combination with spectrum unmixing is low in precision. According to the technical scheme, a random observation matrix is adopted for extracting a small number of samples from original data as compression data. In the reconstruction process, according to an unmixing compressive sensing model, appropriate spectrums are selected from a spectrum library as an end member matrix in the model, then the three-dimensional total variation sparse prior of an abundance value matrix is introduced, and the abundance value matrix is accurately solved through solving a limited linear optimization problem. Finally, a linear mixing model is used for reconstructing the original data. When the compression ratio of urban data shot through a HYICE satellite is 1:20, the normalize mean squared error (NMSE) is smaller than 0.09, when the compression ratio is 1:10,the NMSE is smaller than 0.08, and compared with an existing compressive sensing algorithm, precision is promoted by more than 10%.

Description

technical field [0001] The invention relates to a hyperspectral unmixing compressed sensing method, in particular to a hyperspectral unmixing compressed sensing method based on three-dimensional total variation sparse prior. Background technique [0002] In hyperspectral images, the rich spectral information contains great data redundancy, which seriously increases the resource consumption in the process of hyperspectral image acquisition, transmission and processing. Therefore, it is necessary to design a high-performance hyperspectral image compression algorithm. The existing hyperspectral image compression algorithms are mainly divided into two categories, one is the compression method based on information coding, which mainly uses the common image compression method to remove the redundancy within and between the bands of the hyperspectral image to achieve compression, These include clustering pulse differential coding, 3D wavelet transform, 3D discrete cosine transform...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the shortcomings of the existing joint spectral unmixing hyperspectral image compressive sensing algorithm with low precision, the present invention provides a hyperspectral unmixing compressive sensing method based on three-dimensional total variation sparse prior

Finally reconstruct the original data using a linear mixed model

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