Classification method and system of hyperspectral images

Abstract

The present invention is suitable for the image classification, and provides a classification method of the hyperspectral images. The classification method comprises the steps of dividing the hyperspectral images into a training set and a test set, extracting the local feature points, using a K-means algorithm to calculate the local feature points of the training set to form a dictionary, adoptinga KNN algorithm to form the nearest neighbor words for the to-be-classified local feature points of the test set in the dictionary, searching the nearest neighbor feature points for the to-be-classified feature points of the test set images, searching the neighbor word having the shortest spectral dimension distance in the nearest neighbor words, introducing the triple constraints of the neighborfeature point, the neighbor words and the spectral dimension distance, solving a constraint least absolute fitting problem to obtain a coding coefficient, pooling the coding coefficient by a max-pooling algorithm, and classifying the test set by taking the obtained coding coefficient as a feature descriptor of the hyperspectral images. According to the present invention, an indeterminacy problemwhen a mapping relationship of the hyperspectral image feature points and the dictionary words is established is solved,and the discernment of the similar images is improved.

Description

Technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a classification method and system for hyperspectral images. Background technique [0002] Compared with grayscale images and RGB color images, hyperspectral images contain spatial and spectral information and have a large amount of data. They have important applications in the detection of camouflaged and concealed military targets, as well as in civilian search and rescue detection of targets. With the development of hyperspectral technology, the current hyperspectral images show the characteristics of high spatial resolution. The ground objects have rich texture and structure information on the hyperspectral images, and the spectral information it contains is also very complex and rich. [0003] As a major breakthrough in the field of remote sensing, hyperspectral imagery uses many very narrow electromagnetic wavebands to obtain useful information from objects of...

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the present invention is to provide a hyperspectral image classification method and system, aiming to solve the uncertainty existing in the establishment of the mapping relationship between hyperspectral image feature points and hyperspectral words in the prior art, and the similar image recognition ability is poor The problem

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