Semi-supervision hyperspectral data dimension descending method based on largest neighbor boundary principle

Abstract

The invention discloses a semi-supervision hyperspectral data dimension descending method based on a largest neighbor boundary principle. The method mainly solves the problems that in the prior art, a large amount of supervision information is needed and data discrimination is poor after dimension descending. The method includes the steps that (1) a remote sensing database sample set is divided into a training data set and a mark sample set, (2) a divergence matrix of the mark sample set is generated, (3) a space neighbor matrix of the training data set is generated, (4) a similarity matrix of the training data set is generated, (5) a semi-supervision discrimination item is built by means of the largest boundary principle according to the divergence matrix, (6) a semi-supervision regular term is built, and (7) an optimal projection matrix is obtained by minimizing the sum of the discrimination item and the regular item and accordingly dimension descending is achieved. By means of manifold regularity expressed by a low rank and space regularity of space consistence, the regular item is built; by means of the regular strategy of space and spectrum combination, robustness and completeness of the projection matrix are achieved, data discrimination performance is improved after dimension descending, and the method can be used for classified recognition of hyperspectral data.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a data dimensionality reduction method, which can be used for dimensionality reduction and classification of remote sensing image data. Background technique [0002] Hyperspectral remote sensing technology has been successfully applied in national defense security, environmental monitoring, resource exploration and other fields. It is one of the modern high-tech technologies. However, the development of hyperspectral remote sensing image data processing technology lags behind image imaging equipment and other hardware. This restricts the further popularization and application of hyperspectral remote sensing technology. Classification is an important way to analyze the rich ground object information of hyperspectral remote sensing images and interpret remote sensing information. Therefore, the research on the classification of ground objects in hyperspectral remote ...

AI Technical Summary

Problems solved by technology

Since this method has no supervisory information, the data after dimensionality reduction does not have good discriminative performance.

[0007] (2) Supervised dimensionality reduction methods: such as linear discriminant analysis (LDA), the projection matrix is ​​obtained by maximizing the ratio of the inter-class scatter matrix and minimizing the intra-class scatter matrix. LDA has better discriminative performance than PCA, but LDA reduces The highest dimension after dimension is c-1, and it is not suitable for dimension reduction of non-Gaussian distribution data, which makes the universality worse, where c is the number of sample categories

The existing semi-supervised methods mainly focus on the manifold regularization of the data, without considering the global structure of the data, and ignoring the spatial information of the image data, so that the spatial structure information of the image data cannot be effectively used.

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