Hyper-spectral image classification method based on Gaussian process classifier collaborative training algorithm

A hyperspectral image and Gaussian process technology, which is applied in the field of remote sensing image understanding and interpretation, and hyperspectral image classification, can solve the problems that the classification accuracy is difficult to guarantee, and achieve a small number of labeled samples, high classification accuracy, and improved The effect of accuracy

Inactive Publication Date: 2011-10-05
XIDIAN UNIV
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Problems solved by technology

Although this kind of classifier is more operable, it can only be processed more accurately when the data trai

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  • Hyper-spectral image classification method based on Gaussian process classifier collaborative training algorithm
  • Hyper-spectral image classification method based on Gaussian process classifier collaborative training algorithm
  • Hyper-spectral image classification method based on Gaussian process classifier collaborative training algorithm

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Embodiment Construction

[0033] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0034] Step 1, input hyperspectral image.

[0035] The input hyperspectral image contains N pixels, of which there are l marked pixels, and (N-l) unmarked pixels, each pixel is a sample, and the kth sample uses the feature vector x k express, 1≤k≤N, represents the eigenvector x k The e-th dimension feature of , 1≤e≤d, d is the dimension of the feature vector;

[0036] The above l labeled samples form a labeled sample set The class labels corresponding to the l labeled samples form the class label set the y k ∈{1, K, m}, m is the number of categories of labeled samples, (N-l) unlabeled samples form an unlabeled sample set The above N, l, m, and d are all determined by the specific hyperspectral image;

[0037] Randomly select z unlabeled samples from the unlabeled sample set Q to form the unlabeled sample set U used for collaborative training, denoted as

[003...

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Abstract

The invention provides a hyper-spectral image classification method based on a Gaussian process classifier collaborative training algorithm-. The method belongs to the technical field of image processing, and mainly solves the problem of low classification precision when the number of marked samples of hyper-spectral data is small in the prior art. The method comprises the following implementation processes of: firstly, randomly dividing feature vectors of samples into two sub feature vectors which are used as two visual angles of the samples; performing Gaussian process classifier collaborative training by using the two visual angles of partial non-marked samples and existing marked samples to obtain two final Gaussian process classifiers; and marking the two visual angles of all the non-marked samples by using the two final Gaussian process classifiers respectively, wherein the class mark with higher probability is used as the classification result of the non-marked samples. The method applied to hyper-spectral image classification can be used for remarkably improving the classification accuracy under the condition of small marked sample number.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to the classification of hyperspectral images, and can be used for remote sensing image understanding and interpretation. Background technique [0002] Hyperspectral remote sensing technology appeared in the 1980s. It organically combines the spectral information reflecting the radiation properties of the target with the image information reflecting the spatial and geometric relationship of the target, creating the concept of imaging spectrum. Compared with panchromatic and multi-spectral images, hyperspectral images have rich spectral information of ground features and have great advantages in classification and recognition of ground features. Therefore, it has become an important technical means in the fields of map making, vegetation survey, marine remote sensing, and military intelligence acquisition. [0003] The classification of hyperspectral data is essentially to divide...

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Application Information

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IPC IPC(8): G06K9/66
Inventor 张向荣焦李成王文娜侯彪吴家骥公茂果刘若辰马文萍
Owner XIDIAN UNIV
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