Hyperspectral data subspace projection and classification method based on fuzzy label

Abstract

The present invention discloses a hyperspectral data subspace projection and classification method based on a fuzzy label mainly for solving the problems of wrongly classified ground objects and poor data discrimination performance caused by the mixed pixels and noise in a hyperspectral image. The method comprises the steps of 1, dividing a remote sensing database sample set into a training sample and a labeled sample set; 2, calculating a discrimination term generated by the labeled sample set after the subspace projection; 3, constructing a Laplace regularization term determined by the fuzzy label of the training sample; 4, obtaining an optimal projection matrix and the fuzzy label by maximizing the difference of the discrimination term and the regularization term to realize the effective dimensionality reduction and the high-precision classification simultaneously. According to the present invention, the discrimination term is constructed by a method of discriminating the subspace projection, the data is projected to the low-dimensional space, the data discrimination performance is enhanced, and then the fuzzy label is introduced to construct the Laplace regularization, thereby solving the wrong classification problem brought by the mixed pixels, and realizing the dimensionality reduction and the high-precision classification simultaneously.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a data dimensionality reduction and classification method, which can be used for dimensionality reduction and classification of remote sensing image data. Background technique [0002] After the rapid development of the last century, hyperspectral remote sensing technology has undergone earth-shaking changes in theory, technology and application, and is widely used in agriculture, forestry, national defense reconnaissance, identification and camouflage and other fields. However, the technology of hyperspectral data processing is relatively backward, which restricts the further promotion of hyperspectral remote sensing technology. As an important content of hyperspectral data processing, classification has become a hot spot in the field of hyperspectral data research. [0003] Hyperspectral images can provide a wealth of information. While obtaining spectra to dete...

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However, the current semi-supervised classification methods are often based on "strict clustering assumptions", that is, the assum

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