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

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...

AI Technical Summary

Problems solved by technology

This method first obtains the optimal parameters or data-dependent kernel parameters, and then obtains the optimal semi-supervised classifier, but there are still shortcomings that the available information is not easy to obtain, and the classification accuracy is not high

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