Hyperspectral image classification method based on sparse low-rank regression

Abstract

The invention discloses a hyperspectral image classification method based on sparse low-rank regression, and mainly solves a problem that the processing speed of a hyperspectral image in the prior art is low. The method comprises the steps: (1) reading the hyperspectral image data, and carrying out the mean filtering of the hyperspectral image data; (2) determining a training sample and a testing sample in a spectrum component with a label in the hyperspectral image after filtering; (3) solving a low-rank projection matrix and a parameter matrix according to the training sample; (4) solving an embedded characteristic matrix of the training sample and an embedded characteristic matrix of the testing sample according to the low-rank projection matrix and the parameter matrix; (5) employing a linear supporting vector machine classifier to classify the embedded characteristic matrix of the training sample and the embedded characteristic matrix of the testing sample, and obtaining a classification image. The method is high in classification precision, is low in cost of high-dimensional data processing, and can be used for the discrimination of surface features of the hyperspectral image.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a hyperspectral image classification method, which can be used to distinguish ground objects in hyperspectral images. Background technique [0002] Hyperspectral image object classification is the main content of remote sensing technology processing, based on the following: the same type of pixel has consistency in spectral characteristics and spatial characteristics, and different object types have obvious differences in spectral characteristics and spatial characteristics. Hyperspectral data is a high-dimensional feature space composed of a large number of bands. There is correlation and redundancy between the bands, and a large amount of calculation is required for processing. Moreover, hyperspectral data has a high dimensionality, and the Hughes phenomenon is prone to occur during classification, so reduce the high Spectral data dimensional...

AI Technical Summary

Problems solved by technology

When the existing linear regression method is used for hyperspectral image classification, the classification accuracy is not high and the processing time is long

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