Information extraction method applicable to hyperspectral image

Abstract

The invention relates to an information extraction method applicable to a hyperspectral image, sequentially comprising the following steps of: firstly, acquiring an imaging spectral image; secondly, resampling a ground object sample standard spectral curve; thirdly, finding an obvious wave band of the ground object sample standard spectral curve and judging the wave band; fourthly, resampling theimaging spectral image; fifthly, carrying out secondary resampling on the ground object sample standard spectral curve; sixthly, judging a type of a spectral genetic wave band; seventhly, comparing the ground object sample standard spectral curve with the type of a corresponding wave band in an image element spectral curve of the imaging spectral image, and judging whether the same ground object is represented; and eighthly, obtaining a sum of depths of all the spectral gene wave bands, wherein the larger the sum of the depths of the spectral gene wave bands is, the higher abundance of the ground object on earth surface is. By adopting the information extraction method disclosed by the invention, amount of data processing is reduced, interference of characteristic sum noise of other ground objects is reduced, processing speed and accuracy are improved, and good effect on processing of the hyperspectral image is achieved.

Description

technical field [0001] The invention relates to an information extraction method applicable to hyperspectral images, in particular to an information extraction method applicable to hyperspectral images that involves the classification of spectral bands of ground objects and uses different band types to extract corresponding information. Background technique [0002] With the improvement of hyperspectral image space and spectral resolution, the amount of corresponding data also increases, which requires continuous improvement of hyperspectral image processing methods to adapt to new data processing requirements. For more advanced data acquisition, the original data processing methods have different defects. For example, the advantages of hyperspectral information cannot be fully reflected due to principal component transformation, and the amount of information loss is large; spectral angle analysis and spectral matched filtering are effective for the extraction of mixed pixels...

AI Technical Summary

Problems solved by technology

For more advanced data acquisition, the original data processing methods have different defects. For example, the advantages of hyperspectral information cannot be fully reflected due to principal component transformation, and the amount of information loss is large; spectral angle analysis and spectral matched filtering are effective for the extraction of mixed pixels The accuracy is not high, and it is greatly affected by noise

Images