A Hyperspectral Image Classification Method Based on Label Constrained Elastic Net Graph Model

A technology of hyperspectral image and classification method, which is applied in the field of image information processing to achieve the effect of accurate composition and reduced computational complexity

Active Publication Date: 2021-07-27
NANJING UNIV OF INFORMATION SCI & TECH
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Problems solved by technology

However, the current algorithm does not make full use of label information for image composition. How to construct a graph model and use effective calibration information is the key to a hyperspectral graph semi-supervised classification algorithm.

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  • A Hyperspectral Image Classification Method Based on Label Constrained Elastic Net Graph Model
  • A Hyperspectral Image Classification Method Based on Label Constrained Elastic Net Graph Model
  • A Hyperspectral Image Classification Method Based on Label Constrained Elastic Net Graph Model

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Embodiment Construction

[0032] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0033] The present invention designs a hyperspectral image classification method based on a label-constrained elastic network graph model, such as figure 1 As shown, the steps are as follows:

[0034] S101, performing EMP feature extraction on the hyperspectral image to be treated, and constructing a space-spectrum joint feature;

[0035] S102. Perform label constraint transfer according to the space-spectrum joint feature to obtain a global constraint matrix;

[0036] S103. Construct a dictionary for each pixel according to the global constraint matrix;

[0037] S104, solving the elastic net representation according to the dictionary, and constructing a label-constrained elastic net graph model;

[0038] S105. Perform semi-supervised classification based on the elastic network graph model, obtain a label matrix, and realize hyperspectra...

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Abstract

The invention discloses a hyperspectral image classification method based on a label-constrained elastic net graph model. Steps: perform EMP feature extraction on the hyperspectral image, and construct a space-spectrum joint feature; perform label constraint transfer according to the space-spectrum joint feature to obtain a global constraint matrix; build a dictionary for each pixel point according to the global constraint matrix; solve the elastic net according to the dictionary Representation, construct a label-constrained elastic net graph model; conduct semi-supervised classification based on the elastic net graph model, obtain a label matrix, and realize hyperspectral image classification. The invention can reduce the complexity of calculation, improve the accuracy of composition, and improve the classification performance of the algorithm.

Description

technical field [0001] The invention belongs to the technical field of image information processing, and in particular relates to a hyperspectral image classification method. Background technique [0002] In the 1980s, hyperspectral remote sensing technology began to rise and develop rapidly, and the ability of human beings to observe and understand things on the surface of the earth has undergone a qualitative leap. Hyperspectral remote sensing technology can capture the corresponding spectral information while obtaining the spatial image of the observed ground objects. Therefore, the hyperspectral image is presented as a three-dimensional cube data, realizing the first real map-spectrum integrated imaging. Multiple spectral segments of each pixel form a spectral curve, which contains rich information on the composition of surface objects and can be used to identify different types of surface objects. At present, hyperspectral image classification has become a popular rese...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/28G06F18/241
Inventor 孙玉宝陈逸刘青山陈基伟
Owner NANJING UNIV OF INFORMATION SCI & TECH
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