Figure regular hyperspectral image band selection method based on subspace learning

Abstract

The invention provides a figure regular hyperspectral image band selection method based on subspace learning. The method comprises the concrete realization steps that (1) a hyperspectral image data matrix is inputted; (2) the data matrix is normalized; (3) a figure regular band similarity matrix is calculated; (4) a figure regular band similarity diagonal matrix is calculated; (5) a reconstruction matrix is constructed; (6) the reconstruction matrix is initialized; (7) the number of times of iteration is set; (8) a subspace band selection matrix is calculated; (9) whether the current number of times of iteration is greater than the maximum number of times of iteration is judged, and the step (10) is performed if the judgment result is yes, or one time of current iteration is added and the step (8) is performed; (10) the subspace band selection matrix is outputted; and (11) a subspace data matrix is constructed. A learning mechanism is provided and spectral space geometric structure information is utilized so that the accuracy of band selection can be enhanced.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a subspace learning-based graph-regularized hyperspectral image band selection method in the technical field of hyperspectral imagery (Hyperspectral Imagery) classification. Aiming at the characteristics of too many image redundant bands in hyperspectral image processing, the present invention proposes a band selection method, selects bands containing a large amount of information, removes redundant information, reduces the dimension of the image, and can be used for high The dimension reduction of the spectral image, and the selected bands are also conducive to image classification, which improves the classification accuracy of the hyperspectral image. Background technique [0002] With the continuous development of hyperspectral remote sensing technology at home and abroad, many methods for the selection of hyperspectral image bands have been proposed. [0003] ...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this method is that the method calculates the spectral clustering and the transformation matrix step by step, but the two steps are mutually influenced, so the method lacks a learning mechanism and cannot better select representative band

The disadvantage of this method is that the local structure information of the data is not used in the solution of the low-rank representation coefficient of the method, the low-rank representation coefficient learned is not accurate enough, and the finally selected band is not representative enough.

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