Hyperspectral Image Classification Method Based on Spectral and Neighborhood Information Dictionary Learning

Abstract

The invention discloses a hyperspectral image classification method based on spectrums and neighbourhood information dictionary learning. The defects that in the prior art, only the spectral information of a hyperspectral image is utilized, and hyperspectral image neighbourhood information cannot be effectively utilized for classification are overcome. The method comprises the implementation steps that (1) hyperspectral images are input; (2) a sample set is obtained; (3) a training sample set and a testing sample set are determined; (4) dictionary learning is carried out; (5) the sparse coefficient of the test sample set is obtained; (6) the sparse coefficient is weighted; (7) the hyperspectral images are classified; (8) the classified images are output. The method has the advantage that the classification effect is more precise on the edges and homogeneous regions of the hyperspectral images, and can be used for classifying the hyperspectral images.

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 spectral and neighborhood information dictionary learning in the technical field of hyperspectral image classification. The invention can be used to classify the hyperspectral images. Background technique [0002] The improvement of spatial and spectral resolution of hyperspectral images provides more information for classification, but also brings huge challenges. Traditional classification methods include maximum likelihood classification, decision tree classification, artificial neural network classification, and support vector machine classification. They only classify features from the spectral domain. However, hyperspectral remote sensing data not only contains rich spectral information of ground features, but also has specific descriptions and expressions of ground features in two different dimensions ...

AI Technical Summary

Problems solved by technology

Although this method can quickly classify hyperspectral images, it still has the disadvantage that the neighborhood information of the samples cannot be obtained well by comparing the Euclidean distance to obtain the neighborhood sample set matrix, resulting in poor classification results in homogeneous regions. not good

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