Hyperspectral remote sensing image classification method based on manifold neighbor measurement through local spectral angles

Abstract

The invention discloses a hyperspectral remote sensing image classification method based on manifold neighbor measurement through local spectral angles. A wide range of neighbors are obtained through the traditional Euclidean distance, accurate neighbors are obtained through spectral angles, the local reconstruction is performed through the neighbors and the reconstruction error is minimized, the local reconstruction mode maintains unchanged in the low dimensional space, the reconstruction error is minimized, and accordingly internal identification characteristics in high dimensional data can be extracted. During classification, neighbors of a new sample are obtained through the traditional Euclidean distance, spectral angles between the new sample and the neighbors are calculated, and the new sample is classified as the class with the smallest spectral angles. According to the hyperspectral remote sensing image classification method based on the manifold neighbor measurement through the local spectral angles, the identification characteristics can be effectively extracted, the classification result is accurate, and the feature classification effect on a hyperspectral remote sensing image is good.

Description

technical field [0001] The invention relates to the improvement of hyperspectral remote sensing image feature extraction and classification methods, in particular to a hyperspectral remote sensing image classification method based on local spectral angle manifold neighbors, and belongs to the technical field of hyperspectral remote sensing image feature extraction and classification. Background technique [0002] Scientific researchers proposed hyperspectral remote sensing based on multispectral remote sensing in the early 1980s. The spectral resolution of hyperspectral remote sensing images is as high as 10 -2 λ order of magnitude (belonging to the nanoscale), the band ranges from visible light to short-wave infrared, and the number of spectral bands is as many as dozens or even hundreds. The high spectral resolution of hyperspectral image data makes the interval between adjacent bands narrow , there is an overlapping region of bands, and the spectral channels are no longe...

AI Technical Summary

Problems solved by technology

The SVM algorithm seeks the optimal classification surface between data based on statistics, and linearizes the nonlinear data by mapping it to the kernel function space, thereby simplifying the computational complexity and having a better classification effect; but how to choose Subspace and establishing a suitable model become the difficulty in the application of SVM

[0023] It can be seen from the introduction of the above prior art that the current feature extraction methods and classification methods have their own shortcomings. The feature extraction method cannot effectively extract the identification features. Several classification methods either have many influencing factors or have certain limitations. Both will cause the accuracy of classification results to be affected

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