Graph-based semi-supervised high-spectral remote sensing image classification method

Abstract

The invention relates to a graph-based semi-supervised high-spectral remote sensing image classification method. The method comprises the following steps: extracting the features of an input image; randomly sampling M points from an unlabeled sample, constructing a set S with L marked points, constructing a set R with the rest of the points; calculating K adjacent points of the points in the sets S and R in the set S by use of a class probability distance; constructing two sparse matrixes WSS and WSR by a linear representation method; using label propagation to obtain a label function F<*><S>, and calculating the label prediction function F<*><R> of the sample points in the set R to determine the labels of all the pixel points of the input image. According to the method, the adjacent points of the sample points can be calculated by use of the class probability distance, and the accurate classification of high-spectral images can be achieved by utilizing semi-supervised conduction, thus the calculation complexity is greatly reduced; in addition, the problem that the graph-based semi-supervised learning algorithm can not be used for large-scale data processing is solved, and the calculation efficiency can be improved by at least 20-50 times within the per unit time when the method provided by the invention is used, and the visual effects of the classified result graphs are good.

Description

Graph-based semi-supervised hyperspectral remote sensing image classification method technical field The invention belongs to the technical field of image processing and relates to the classification of hyperspectral remote sensing images, which can be used for preprocessing hyperspectral remote sensing images, in particular to a graph-based semi-supervised hyperspectral remote sensing image classification method. Background technique Hyperspectral remote sensing images have high spectral resolution and provide rich information about the types of ground objects. The classification of remote sensing images is one of the key technologies for the analysis and application of remote sensing images. How to deal with the massive data and high-dimensional characteristics of hyperspectral images and combine various features of hyperspectral images to study fast and efficient target recognition and classification algorithms It is a hotspot in hyperspectral image processing research ...

AI Technical Summary

Problems solved by technology

For the classification of hyperspectral remote sensing images, due to the small number of marked sample points, it is difficult to obtain better classification results using traditional supervised learning algorithms, and it is difficult to obtain better classification results without using label information in unsupervised methods classification result

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