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

A discriminant analysis and image classification technology, which is applied in computer parts, character and pattern recognition, complex mathematical operations, etc., can solve the problems of only focusing on linear feature extraction and ignoring the nonlinear structural characteristics of hyperspectral remote sensing image data

Active Publication Date: 2019-04-30
LIAONING TECHNICAL UNIVERSITY
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Problems solved by technology

However, PCA, LDA and LFDA methods only focus on linear feature extraction but...

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

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[0063] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0064] In this embodiment, a hyperspectral image is taken as an example, and the hyperspectral image classification method based on wavelet kernel local Fisher discriminant analysis of the present invention is used to classify the hyperspectral image.

[0065] A hyperspectral image classification method based on wavelet kernel local Fisher discriminant analysis, such as figure 1 shown, including the following steps:

[0066] Step 1: Read in the hyperspectral remote sensing image dataset;

[0067] In this embodiment, a part of the hyperspectral data of the image formed by the Airborne Reflective Optics Spectrographic Imaging System (ROSIS-03) in Germany on Pavia Universit...

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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...

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Application Information

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IPC IPC(8): G06K9/62G06F17/16
CPCG06F17/16G06V20/194G06F18/2132G06F18/214G06F18/2411
Inventor 吕欢欢张辉刘万军
Owner LIAONING TECHNICAL UNIVERSITY
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