Spectral line inflexion multi-scale optimizing segmentation method and application thereof

Abstract

The invention belongs to the field of hyperspectral remote sensing application and in particular relates to a spectral matching and recognition method. The invention overcomes the defects that the conventional spectral matching and recognition method only considers the whole similarity measurement between spectral lines but neglects the local difference measurement between the spectral lines, and provides a spectral segmentation-based matching and recognition method. The method comprises the following steps of: firstly, performing transformation processing on the spectral lines by adopting the multi-scale wavelet transform taking a second-order Gaussian derived function as a wavelet function; secondly, extracting an optimized inflexion of a spectral curve by using the designed inflexion multi-scale optimizing algorithm; and finally, segmenting the spectral lines based on the extracted optimized inflexion information, and recognizing the spectral lines by adopting a segmentation matching method. The spectral matching and recognition method has the advantages that: wave bands with larger ground object spectrum difference and wave bands with smaller spectrum difference can be segmented into different segmentations through the inflexion segmentation so as to protrude the wave bands with the larger spectrum difference and enhance the effectiveness of spectral matching and recognition.

Description

technical field [0001] The invention belongs to the application field of hyperspectral remote sensing, and in particular relates to a method for multi-scale optimization and segmentation of inflection points of spectral lines and its application in spectral matching and identification. Background technique [0002] In hyperspectral image processing, spectral matching technology is one of the key technologies for hyperspectral object classification and recognition. Spectral matching judges the category of ground objects by comparing the spectral curves that reflect the spectral radiation characteristics of the ground objects. Currently commonly used spectral matching methods are generally based on the overall similarity measure of spectral curves. However, in hyperspectral images, the characteristics of different ground objects may be similar, usually appearing as spectral lines in certain bands. Very similar, even the same, but there are obvious differences in some other ba...

AI Technical Summary

Problems solved by technology

Currently commonly used spectral matching methods are generally based on the overall similarity measure of spectral curves. However, in hyperspectral images, the characteristics of different ground objects may be similar, usually appearing as spectral lines in certain bands. Very similar, or even the same, but with noticeable differences in some other bands

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