Hyperspectral image classification method and system based on correlation entropy principle

A technology of hyperspectral images and classification methods, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as difficulty in learning effective classification features and high sample complexity, and achieve the effect of small sample complexity

Active Publication Date: 2018-12-18
HUAZHONG NORMAL UNIV
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Problems solved by technology

[0007] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art, and propose a hyperspectral image classification method based on the principle of correlation entropy, to solve the problem that th...

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  • Hyperspectral image classification method and system based on correlation entropy principle
  • Hyperspectral image classification method and system based on correlation entropy principle
  • Hyperspectral image classification method and system based on correlation entropy principle

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[0050] The technical solutions and effects of the present invention will be described in further detail below with reference to the accompanying drawings.

[0051] refer to figure 1 , the implementation steps of the present invention are as follows:

[0052] 1) Input a hyperspectral image, and normalize the image so that the range is within [0, 1]. Order I tr ={I 1 , I 2 ,Λ,I N} is a training set consisting of N pixels, where I i ∈ R d (i=1, 2, Λ, N) is the i-th training sample, and they belong to class C; normalize the image, and normalize the data value to [0, 1] by the following steps:

[0053]

[0054] Among them, M x =max(I(:)),M n =min(I(:)) are the maximum and minimum values ​​of pixel values ​​on the input image, respectively, and is the pixel I with coordinates (i,j) ij B bands, is the pixel with coordinates (i, j) on the normalized hyperspectral image B bands.

[0055] 2) Select p% of the pixels from the hyperspectral image as training samples...

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Abstract

The invention provides a hyperspectral image classification method and a system based on the correlation entropy principle. Firstly, the input image is preprocessed so that the element values of all spectral vectors are between 0 and 1; secondly, a small number of samples are selected as training samples, and the spatial features of hyperspectral images are extracted by using the designed hierarchical model of fusion dimension reduction method and correlation entropy principle. Then, the spatial spectrum characteristics of the test samples are learned by using the trained hierarchical structure. Finally, the characteristics of the test samples are input into the KNN classifier to get the class label. The invention fully combines the spectral and spatial characteristics of the hyperspectraldata by using the correlation entropy principle; by using the hierarchical model of the invention, more abstract spatial spectrum features can be obtained; due to the characteristics of LDA, the invention has small sample complexity, and only a small number of training samples are needed to obtain a better classification result, so the invention is more conducive to practical application.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a hyperspectral image classification method and system, which can be used for meteorological monitoring, environmental monitoring, land utilization, urban planning, and disaster prevention and mitigation. Background technique [0002] With the development of spectral imaging technology, hyperspectral remote sensing technology plays an increasingly important role in the earth observation systems of many countries around the world. Hyperspectral images can provide rich spectral information and have been widely used in geological surveys, resource exploration, environmental monitoring, and precision agriculture. Among many applications, hyperspectral image classification has attracted much attention as a common technique. However, due to the characteristics of hyperspectral images, such as high data dimensionality, few training samples, and large spatial variat...

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Application Information

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IPC IPC(8): G06K9/62G06K9/66
CPCG06V30/194G06F18/241
Inventor 魏艳涛余书剑姚璜师亚飞赵刚童名文
Owner HUAZHONG NORMAL UNIV
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