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Hyperspectral image band selection method and system based on neighbor subspace division

A technology of band selection and hyperspectral, which can be used in the analysis of materials, material analysis by optical means, instruments, etc., and can solve problems such as not meeting actual needs.

Active Publication Date: 2021-07-06
ZHEJIANG NORMAL UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

These two situations obviously do not meet the actual needs

Method used

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  • Hyperspectral image band selection method and system based on neighbor subspace division
  • Hyperspectral image band selection method and system based on neighbor subspace division
  • Hyperspectral image band selection method and system based on neighbor subspace division

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Embodiment 1

[0052] This embodiment provides a method for selecting a hyperspectral image band based on neighbor subspace division, such as figure 1 , 2 shown, including:

[0053] S11. Input the hyperspectral image cube, and calculate the correlation coefficient between any two adjacent bands contained in the input hyperspectral image cube, and obtain the vector of the correlation coefficient;

[0054] S12. Find all the correlation coefficient extreme points according to the obtained correlation coefficient vector, and filter out the correlation coefficient minimum value points from all the correlation coefficient extreme value points found, and determine the minimum value point of the correlation coefficient through the selected correlation coefficient minimum value points. Well-divided hyperspectral subspace;

[0055] S13. Sort the subspaces according to the size of the number of bands in the subspace, calculate the information entropy of the bands in each subspace, and select a requir...

Embodiment 2

[0091] The difference between the method for selecting a hyperspectral image band based on neighbor subspace division provided in this embodiment and the first embodiment is that:

[0092] In this embodiment, in order to verify the effectiveness of the proposed SEASP algorithm in hyperspectral band selection, a large number of related experiments are carried out.

[0093] In the experiment, three public datasets are mainly selected, namely the Botswana dataset, the Salinas dataset and the Indian Pines dataset. The selected classifiers are KNN, SVM and LDA. The parameter k of the KNN classifier is set to 5. The distance function uniformly used by SVM is a Gaussian kernel function. The penalty coefficient of the Indian Pines and Botswana datasets is 10000, and the penalty coefficient of the Salinas dataset. is 100. Since the above three classifiers are all supervised classification methods, 10% of the entire data set is randomly selected as training samples and the rest of the ...

Embodiment 3

[0103] This embodiment provides a hyperspectral image band selection system based on neighbor subspace division, including:

[0104] The calculation module is used to input the hyperspectral image cube, and calculate the correlation coefficient between any two adjacent bands contained in the input hyperspectral image cube, and obtain the vector of the correlation coefficient;

[0105] The screening module is used to find all the correlation coefficient extreme points according to the obtained correlation coefficient vector, and filter out the correlation coefficient minimum value points from all the found correlation coefficient extreme value points, and pass the selected correlation coefficient minimum value points. point to determine the optimally divided hyperspectral band subspace;

[0106] The selection module is used to sort the subspaces according to the size of the number of bands in the subspace, calculate the information entropy of the bands in each subspace, and sel...

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Abstract

The invention discloses a hyperspectral image wave band selection method and system based on neighbor subspace division, and the method comprises the steps: S11, inputting a hyperspectral image cube, calculating a correlation coefficient between any two adjacent wave bands included in the input hyperspectral image cube to obtain a vector of the correlation coefficient; S12, searching for all correlation coefficient extreme points according to the obtained vectors of the correlation coefficients, screening out correlation coefficient minimum points from all the searched correlation coefficient extreme points, and determining an optimally divided hyperspectral band subspace through the screened correlation coefficient minimum points; and S13, sorting the subspaces according to the number of the wavebands in the subspaces, calculating the information entropy of the wavebands in each subspace, and selecting a required number of characteristic wavebands from the subspaces according to the calculated information entropy. According to the invention, it can be ensured that the finally selected wave band contains relatively complete information.

Description

technical field [0001] The invention relates to the technical field of hyperspectral remote sensing image band selection, in particular to a method and system for selecting a hyperspectral image band based on neighbor subspace division. Background technique [0002] Compared with traditional RGB images, hyperspectral images have the characteristics of rich information, large number of bands, and high resolution, so they are widely used in target detection, environmental monitoring, mineral exploration, agricultural resources survey and marine research. Because it contains a large amount of band information, and the feature similarity between adjacent bands is high, this increases the computational complexity of hyperspectral image classification to a certain extent, and also affects the final classification accuracy of the classifier. Therefore, it is necessary to reduce the dimensionality of hyperspectral images to solve the problem of dimensional disaster. [0003] Featur...

Claims

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Application Information

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IPC IPC(8): G01N21/17G01N21/01
CPCG01N21/17G01N21/01G01N2021/1793G06V20/194G06V10/764G06F18/24147Y02A40/10
Inventor 朱信忠徐慧英王俊唐厂赵建民
Owner ZHEJIANG NORMAL UNIVERSITY
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