A Hyperspectral Image Processing Method for Epidote Information Extraction

Abstract

The invention belongs to an information extraction method, in particular to a hyperspectral image processing method for epidote information extraction. It includes step 1: band sampling, step 2: judgment, step 3: calculation, using the judgment result of step 2 to calculate and obtain the abundance. The effect of the present invention is: the present invention resamples the hyperspectral image, extracts a specific band, performs a series of judgments and calculations, and can accurately calculate the abundance values ​​of kaolin in different regions within the image range.

Description

technical field [0001] The invention belongs to an information extraction method, in particular to a hyperspectral image processing method for epidote information extraction. Background technique [0002] Extracting the abundance of different types of minerals in the image area by processing image information is a commonly used method in the field of exploration. This method does not require staff to go directly to the field for exploration and sampling, and can extract the abundance of different types of minerals. The current methods for extracting epidote information from hyperspectral remote sensing images are mainly spectral matching of full bands or partial continuous bands. The specific methods include spectral angle, hybrid demodulation filtering, and hourglass extraction methods. Most of them are composed of a single mineral. These methods are easily affected by the spectrum or noise of other ground objects in the process of information extraction, and the accuracy ...

AI Technical Summary

Problems solved by technology

The current methods for extracting epidote information from hyperspectral remote sensing images are mainly spectral matching of full bands or partial continuous bands. The specific methods include spectral angle, hybrid demodulation filtering, and hourglass extraction methods. Most of them are composed of a single mineral, and these methods are susceptible to the influence of other ground object spectra or noise in the process of information extraction, and the accuracy of information extraction 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.