Projection structure sparse coding-based hyperspectral image classification method

Abstract

The invention discloses a projection structure sparse coding-based hyperspectral image classification method, which mainly solves the problem that in the prior art, neighborhood information of a hyperspectral image can not be effectively used for classification. The method comprises the realizing steps of: (1) reading in hyperspectral image data; (2) determining a training sample set and a test sample set in spectral vectors with labels of the hyperspectral image; (3) solving a projection matrix according to the training sample set; (4) according to the projection matrix of training samples of the hyperspectral image, solving a sparse coefficient of the test sample set; (5) according to the projection matrix and the sparse coefficient of the test sample set, judging a test sample by using an error judgment function, thus obtaining a surface feature category label of the test sample. The method has the advantages of high classification precision, small processing expenditure cost of high dimensional data and usage for surface feature differentiation of the hyperspectral image.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a hyperspectral image classification method, which can be used to distinguish ground objects in hyperspectral images. Background technique [0002] Hyperspectral image object classification is the main content of remote sensing technology processing, based on the following: the same type of pixel has consistency in spectral characteristics and spatial characteristics, and different object types have obvious differences in spectral characteristics and spatial characteristics. Hyperspectral image is a mass data of map integration generated by multi-spectral remote sensing imaging equipment, which contains spatial information of ground objects and non-negative spectral information. Each point in the image can be described by a high-level spectral vector composed of spectral information of many spectral bands, so hyperspectral data is characterized...

AI Technical Summary

Problems solved by technology

However, this classification method is carried out separately for dimension reduction and classification, which is prone to errors and limits the classification accuracy to a certain extent.

At the same time, there are also problems such as long data processing time and insufficient use of neighborhood information.

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