A Hyperspectral Image Processing Method for Pyrophyllite Information Extraction

Abstract

The invention belongs to a hyperspectral image processing method, in particular to a hyperspectral image processing method for pyrophyllite information extraction. It includes: step 1, preprocessing; step 2: sampling, sampling images with wavebands at 2045nm, 2075nm, 2090nm, 2105nm, 2165nm, 2195nm, 2240nm, 2315nm, 2330nm, 2375nm, 2390nm; step 3: judging, and step 4 :calculate. The effect of the method of the present invention is: only 11 wave bands are used, such as the SASI full-band hyperspectral image has 101 wave bands, the amount of data processed by this method can be reduced by 89%, and because the computer automatically extracts in one step, reducing the main Operation steps such as component transformation and endmember spectrum selection can increase the operation speed by more than 9 times. Since most of the bands that have little relation to information extraction are removed, the interference of other substances or noise on its spectrum is reduced, and the accuracy of information extraction is improved. It has a good effect and significance on the rapid extraction of pyrophyllite information in hyperspectral image data.

Description

technical field [0001] The invention belongs to a hyperspectral image processing method, in particular to a hyperspectral image processing method for pyrophyllite information extraction. Background technique [0002] The current pyrophyllite information extraction method for hyperspectral remote sensing images is mainly spectral matching of full bands or partial continuous bands. In the process of information extraction, these methods are easily affected by the spectrum or noise of other ground features, and the accuracy of extracted information is relatively low. Secondly, the existing spectrum extraction method has many manual steps, which increases the error of human judgment. The third is that hyperspectral data has many bands and a large amount of data. The existing methods take a long time to process, which reduces the speed of data processing and the scale of application. Therefore, how to reduce the influence of other ground objects or noise, manual operation steps...

AI Technical Summary

Problems solved by technology

[0002] The current pyrophyllite information extraction method for hyperspectral remote sensing images is mainly spectral matching of full bands or partial continuous bands. Mineral composition, these methods are easily affected by the spectrum or noise of other ground objects in the process of information extraction, and the accuracy of extracted information is relatively low

Secondly, the existing spectrum extraction method has many manual steps, which increases the error of human judgment.

The third is that the hyperspectral data has many bands and a large amount of data. The existing methods take a long time to process, which reduces the speed of data processing and the scale of application.