Hyper-spectral image classification method based on singular value decomposition and neighborhood space information

Abstract

The invention discloses a hyper-spectral image classification method based on singular value decomposition and neighborhood space information, comprising the following steps: inputting a training sample matrix of each category, and carrying out singular value decomposition on the training sample matrixes to get a right singular matrix corresponding to jth-category training samples; for data in the training sample matrixes, using a least squares method to calculate the residual of the data corresponding to each category, comparing the residuals of the data corresponding to all the categories, and classifying the data to the category corresponding to the smallest residual; and repeating the steps to get the category of each training sample, comparing the category of each training sample with the original category, getting a parameter making the classification accuracy rate of the training samples highest through iterative comparison, classifying each data in a test sample matrix, and outputting a classification result matrix. Through the classification method, the precision of classification is improved, the time for classification is shortened, and the level of automation of hyper-spectral image classification is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a hyperspectral image classification method based on singular value decomposition and neighborhood space information. Background technique [0002] Most hyperspectral image supervised classification methods need enough training samples to support the classification results, such as linear discriminant analysis, naive Bayesian, neural network, etc. Sample acquisition is often time-consuming and expensive. The support vector machine method can also obtain high classification accuracy in the case of small samples, but the support vector machine needs to select a suitable kernel function and its corresponding parameters. Different parameters often have a greater impact on the classification results, and through cross-validation Obtaining appropriate parameters by other means often consumes a lot of training time. Contents of the invention [0003] The purpose of the pres...

AI Technical Summary

Problems solved by technology

Sample acquisition often takes a lot of time and money

The support vector machine method can also obtain high classification accuracy in the case of small samples, but the support vector machine needs to select a suitable kernel function and its corresponding parameters. Different parameters often have a greater impact on the classification results, and through cross-validation It often takes a lot of training time to obtain suitable parameters by other means.