Mixed pixel decomposition method of high-spectral remote sensing image

Abstract

The invention discloses a mixed pixel decomposition method of a high-spectral remote sensing image. A multi-scale non-negative-matrix decomposition algorithm of approximately sparsity constraint is applied to demixing of the high-spectral remote sensing image, and a new approximate sparse model replaces an L0 model for solution aimed at the problem that the L0 model in the sparsity de-mixing technology of the high-spectral remote sensing image is hard to solve, and on such basis, the complex multi-scale space geometric structure of the high-spectral remote sensing image is considered, a totalvariation space constraint is added, and the decomposition precision and performance of mixed pixels are improved obviously.

Description

technical field [0001] The invention relates to remote sensing image processing technology, in particular to a hyperspectral remote sensing image mixed pixel decomposition method. Background technique [0002] Because hyperspectral data can simultaneously record information of hundreds of spectral segments in the same scene, it is widely used in many fields. However, due to the low spatial resolution of the hyperspectral sensor and the complex diversity of ground objects, the spectra of spatially adjacent substances are inevitably fused together, resulting in the mixing phenomenon of hyperspectral data, which greatly affects the ground. Accuracy of object recognition and differentiation. Hyperspectral unmixing technology can decompose these mixed pixels into typical ground object spectra (endmembers) and the proportion of corresponding spectra (abundance). [0003] The linear hyperspectral unmixing technique is a standard technique for spectral unmixing. It can decompose t...

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Problems solved by technology

[0008] The technical problem to be solved by the present invention is to overcome the deficiencies of the existing technologies, provide a hyperspectral remote sensing image mixed pixel decomposition method, aiming at the difficult problem of solving the L0 model in the existing hyperspectral remote sensing image sparse unmixing technology, using a new The approximate sparse model replaces the L0 model for solving; and on this basis, considering the complex multi-scale spatial geometry structure of hyperspectral remote sensing images, the multi-scale total variation space constraints are added, so that the unmixing accuracy and performance are significantly improved

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