A hyperspectral image classification method based on wavelet kernel local Fisher discriminant analysis

Abstract

The invention provides a hyperspectral image classification method based on wavelet kernel local Fisher discriminant analysis, and relates to the technical field of hyperspectral images. The method comprises the following steps: firstly, reading hyperspectral data as sample data, and normalizing a sample data set; mapping the data from a low-dimensional original space to a high-dimensional featurespace by adopting a wavelet kernel function; performing feature extraction on the sample data by using a local Fisher discriminant analysis method; dividing the data set after dimension reduction into training data and test data, and inputting the training data into an SVM classifier to obtain an optimal parameter value; inputting the test data into a classifier to obtain a classification result;and performing analysis and precision evaluation on a classification result. According to the hyperspectral image classification method based on wavelet kernel local Fisher discriminant analysis provided by the invention, a very good classification effect is obtained, and the method can be applied to the fields of agricultural monitoring, environmental management, disaster assessment, mineral mapping and the like.

Description

technical field [0001] The invention relates to the technical field of data processing and application of hyperspectral images, in particular to a hyperspectral image classification method based on wavelet kernel local Fisher discriminant analysis. Background technique [0002] Hyperspectral remote sensing refers to the science and technology of remote sensing data acquisition, processing, analysis and application with high spectral resolution. It is one of the important research directions in the field of remote sensing in the 21st century. Compared with multispectral remote sensing, hyperspectral remote sensing can obtain information of hundreds of continuous spectral segments of ground objects, and these rich spectral information can enhance the ability to distinguish ground objects. Hyperspectral remote sensing plays an important role in national defense construction, map civilian economy, etc., and has been widely used in target detection, surface classification, enviro...

AI Technical Summary

Problems solved by technology

However, PCA, LDA and LFDA methods only focus on linear feature extraction but ignore the nonlinear structural features of hyperspectral remote sensing image data.

Images