Hyperspectral image classification based on gradient lifting decision tree and semi-supervise algorithm integration

A hyperspectral image and decision tree technology, applied in computing, computer parts, instruments, etc., can solve problems such as the decline of classifier learning performance, the need to improve the classification accuracy, and the inaccurate prediction of class labels, so as to ensure the accuracy of classification, The effect of reducing the number and improving the accuracy

Active Publication Date: 2016-12-07
XIDIAN UNIV
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However, the disadvantage of semi-supervised learning is that when the number of samples is small and the model training is insufficient, the class label prediction of unlabeled data is often inaccurate, and adding wrongly labeled samples to the training set will lea

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  • Hyperspectral image classification based on gradient lifting decision tree and semi-supervise algorithm integration
  • Hyperspectral image classification based on gradient lifting decision tree and semi-supervise algorithm integration

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[0028] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0029] refer to figure 1 , the specific implementation steps of the present invention include:

[0030] Step 1, input hyperspectral image data:

[0031] Input a hyperspectral image, remove the background sample points, and there are N remaining sample points, including C categories.

[0032] Step 2, sample point spatial spectrum feature extraction, the implementation steps are:

[0033] In step 2a, the spectral feature values ​​of each band of each sample point are used as the spectral feature vector of the sample point, and the original feature dimension of the sample point is d.

[0034] Step 2b, take the neighborhood window for each sample point, the window size is c×c, take the maximum value of each dimension feature of all sample points in the window as the spatial feature of the central sample point, and the feature dimension is d.

[0035] In...

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Abstract

The invention discloses a hyperspectral image classification based on gradient lifting decision tree and semi-supervise algorithm integration in order to solve the technical problem that hyperspectral image classification based on active learning and semi-supervise learning is low in classification precision. Hyperspectral image classification includes the steps that firstly, hyperspectral image data is input; secondly, features of sample points are extracted; thirdly, parameters of a gradient lifting decision tree classifier are trained; fourthly, massed learning sample points are classified; fifthly, the confidence degree of the sample points are assessed; sixthly, the sample points are screened through sparse representation; seventhly, a marked training set is updated; eighthly, a classification result is output. Assessment is conducted on the confidence degree of the unmarked sample points through the prediction result of the classifier and sparse representation, according to the confidence degree of the unmarked sample points, the sample points are divided into two sets for different kinds of processing, burdens for manual marking are reduced while classification precision is improved, and hyperspectral image classification can be used in the fields of geological survey, atmospheric pollution and like.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a hyperspectral image classification method, in particular to a hyperspectral image classification method based on gradient lifting decision tree semi-supervised algorithm fusion, which can be used for geological surveys, air pollution and military target strikes, etc. field. Background technique [0002] With the development of optical remote sensing technology, the history of remote sensing imaging has evolved from panchromatic (black and white) images, color photography, multi-spectral scanning imaging to today's hyperspectral remote sensing imaging and hyperspectral imaging. Hyperspectral remote sensing technology uses 10 -2 The λ and continuous spectral channel performs continuous remote sensing imaging of ground objects, obtains a large amount of ground object image data with complete spectral information, and realizes the simultaneous acquisition of ground object s...

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Application Information

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IPC IPC(8): G06K9/62G06K9/00
CPCG06V20/194G06V20/13G06F18/2113G06F18/214G06F18/241
Inventor 张向荣焦李成张鑫冯婕白静马文萍侯彪马晶晶
Owner XIDIAN UNIV
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