Method for classifying hyperspectral images based on semi-supervised conditional random field

A technology of hyperspectral image and conditional random field, applied in the field of image processing, can solve the problems of difficult acquisition of information and low classification accuracy, achieve good classification effect and overcome the effect of low classification accuracy

Active Publication Date: 2012-06-27
XIDIAN UNIV
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This method first obtains the optimal parameters or data-dependent kernel parameters, and then obtains the optimal semi-supervised class

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  • Method for classifying hyperspectral images based on semi-supervised conditional random field
  • Method for classifying hyperspectral images based on semi-supervised conditional random field
  • Method for classifying hyperspectral images based on semi-supervised conditional random field

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[0041] Reference figure 1 The specific implementation steps of the present invention are as follows.

[0042] Step 1. Initialize, follow the steps below:

[0043] The first step is to find the average value of all pixels in the matrix RHSI corresponding to the original data according to the following formula:

[0044] k = X i X j X h ( RHSI ) / N

[0045] Among them, k is the average value of all pixels, ∑ represents superposition, i represents the number of matrix rows, j represents the number of matrix columns, and h represents the number of bands. Overlay for all rows, Overlay for all columns, For the superposition of all bands, RHSI is the matrix corresponding to the original data, and N=i*j*h represents the total number of pixels;

[0046] The second step is to square the total number N of pixels;

[0047] The third step is to subtract the pixel average value k for each pixel, and then divide by the value obtained by the square root of the to...

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Abstract

The invention discloses a method for classifying hyperspectral images based on a semi-supervised conditional random field. The method comprises the following steps of: (1) initializing; (2) inputting a training sample set corresponding to the object classification of the to-be-classified hyperspectral images; (3) training parameters of a unitary potential information amount; (4) inputting to-be-classified test data; (5) classifying in advance; (6) confirming an average value of all spectral vectors in the training sample set and the to-be-classified test data; (7) updating a training sample; (8) updating the parameters of the unitary potential information amount; (9) training the parameters of a binary potential information amount; (10) confirming a posterior probability of the unitary potential information amount; (11) confirming the posterior probability of the binary potential information amount; (12) updating the information; and (13) confirming classification. A semi-supervised theory is adopted in the invention; the effect for the condition of insufficient training sample is especially obvious; and the method has the advantages of low calculation complexity, high classifying accuracy and wide adaptability.

Description

Technical field [0001] The present invention belongs to the technical field of image processing, and further relates to a hyperspectral image classification method based on a semi-supervised conditional random field in the field of target recognition. This method is mainly aimed at the problem that when the training sample is small, the hyperspectral image classification method using conditional random fields may cause the classification accuracy to decrease. It can be applied to the area corresponding to different feature categories in the hyperspectral image when the training sample is small. classification. Background technique [0002] Hyperspectral images can simultaneously image the surface area through hundreds of continuous and subdivided spectral bands to obtain three-dimensional image data. Hyperspectral images contain rich spectral information, and show very strong spectral correlation and spatial correlation, which provides the possibility for feature extraction, tar...

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Application Information

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IPC IPC(8): G06K9/62
Inventor 焦李成侯彪刘瑞清张向荣马文萍王爽
Owner XIDIAN UNIV
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