High-spectral image sharpening method based on probability matrix decomposition

Abstract

The invention discloses a high-spectral image sharpening method based on probability matrix decomposition, and belongs to the field of remote sensing image processing. The method is characterized in that the method comprises the steps: carrying out the preprocessing of two inputted images based on the hypothesis that a pixel spectrum vector of a high-resolution high-spectral image is just formed by the linear superposition of a few of vectors with the hidden spectrum features according to one low-resolution high-spectral image and one high-resolution high-spectral image and the frequency response matrix, decomposition matrix dimensions and algorithm iteration number, corresponding to the high-spectral images, of a multispectral camera, wherein the low-resolution high-spectral image and the high-resolution high-spectral image are taken at the same height in the same target region at the same time; listing mathematical equations of the two processed images and a to-be-solved high-resolution high-spectral image, and building a Bayesian model; calculating the posteriori probability distribution of the decomposition matrix, and obtaining a matrix with the hidden spectrum features in the decomposition matrix, and solving the mean value of linear superposition vectors corresponding to the two images after preprocessing, thereby obtaining the to-be-solved high-resolution high-spectral image. The method greatly reduces the time consumption of calculation while improving the sharpening precision, and is easy to adjust.

Description

technical field [0001] The invention relates to a hyperspectral image sharpening method based on probability matrix decomposition, which belongs to the field of remote sensing image processing. Background technique [0002] Spectral image refers to a collection of multiple images formed on different spectral bands for the same shooting target. According to the spectral resolution of image imaging (spectral resolution refers to the wavelength interval of different light waves that the instrument can identify; high spectral resolution refers to the small interval between different light waves that can be distinguished, and the standards for different remote sensing satellites are different. High The standard of spectral resolution is also different, here is only a general reference, the same below), spectral image is divided into multi-spectral image (resolvable wavelength difference is 100nm), hyperspectral image (resolvable wavelength difference is 10nm) and ultra-spectral i...

AI Technical Summary

Problems solved by technology

[0004] 1. The sharpening accuracy and calculation time consumption of existing algorithms still need to be improved. Although most algorithms have high sharpening accuracy, the calculation time consumption has doubled;

[0005] 2. Most algorithms use optimization modeling. There are many parameters in the model, which need to be adjusted manually through experiments. Once the adjustment is not done properly, the sharpening accuracy will decrease, and the adjustment process is complicated and time-consuming

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