Spectral angle mapping method used for correcting negative correlation of hyperspectral remote sensing image by wavebands

Abstract

The invention provides a spectral angle mapping method used for correcting the negative correlation by wavebands, which is technically characterized in that: the traditional spectral angle is calculated, the value of the newly added spectral bands is used as the independent variable, the existence of the negative correlation on each newly added spectral band is judged, and the newly added spectral band with the negative correlation is provided with correction parameters. The spectral angle mapping method provided by the invention aims to solve the problem that the traditional spectral angle mapping method can not distinguish the negative correlation of spectrums so that the spectral curves with different characteristics are classified into the same category relative to a certain reference spectrum. The experiments proves that: due to the adoption of the spectral angle mapping method provided by the invention, the separability of the spectrums is improved effectively, and the spectral vectors which can not be separated by using the traditional spectral angle mapping method can be separated by stages according to the difference of the generated negative-correlation wavebands. The spectral angle mapping method provided by the invention laids a foundation for the subsequent hyperspectral remote sensing image classification, the target identification and the like.

Description

technical field [0001] The invention relates to a spectral angle mapping method for correcting negative correlation band by band of hyperspectral remote sensing images, which is used for spectral similarity measurement of hyperspectral remote sensing images, and belongs to the field of hyperspectral remote sensing digital image processing and pattern recognition. The spectral angle after correcting the negative correlation band by band can better reflect the similarity relationship between similar spectra and enhance the separability between spectra. applications such as recognition. Background technique [0002] The hyperspectral data of a certain region that can be acquired by hyperspectral imagers contains multiple information such as space, spectrum, and radiation, and is a cutting-edge technology in the field of remote sensing. Classification is an important research direction in the field of hyperspectral image processing, and it has a wide range of applications in th...

AI Technical Summary

Problems solved by technology

[0004] However, SAM finally uses a scalar to represent the similarity between dozens or even hundreds of band spectral vectors, which has the following limitations: all spectra in SAM are involved in the calculation, and the similarity of most secondary spectra will suppress the difference between a few characteristic spectra. The final output of SAM is relatively small due to the difference between them, which brings difficulties to the subsequent classification and the setting of the threshold between classes; SAM is sensitive to the additive factor of each spectrum, which will bring uncertainty to the final output ;SAM cannot distinguish the positive and negative correlation between two spectral vectors, which makes it possible for different spectra to obtain the same angle value relative to a certain reference spectrum, which eventually leads to misclassification and high false alarm rate.

[0005] The above-mentioned improved algorithms can optimize the performance of the SMA algorithm to a certain extent, but they also have their own shortcomings. The subset of bands selected by BAO-SAM may not be the best subset to characterize the entire spectrum; The spectral value is changed on the surface, which destroys the corresponding relationship between the spectrum and the actual ground objects; KSAC and NSAM amplify the spectral angle value through nonlinear mapping, and also amplify the influence of negative correlation

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