Hyperspectral image characteristic extraction algorithm based on manifold learning linearization

Abstract

The invention discloses a hyperspectral image characteristic extraction algorithm based on manifold learning linearization and belongs to the technical field of hyperspectral image data processing and application. The shortcoming that a manifold learning algorithm has no generalization ability is overcome through the improved manifold learning linearization algorithm. The method comprises the steps that first, a preliminary dimensionality reduction result and a Laplacian matrix are computed; second, a matrix equation set constant term matrix and a coefficient matrix are established; third, a characteristic converting matrix is computed; and fourth, a final dimensionality reduction result is computed according to the characteristic converting matrix. The shortcoming that the global linear mapping hypothesis in LPP, NPE and LLTSA linearization manifold learning algorithms is invalid most of the time is overcome, a penalty term which deviates from an original manifold learning algorithm result is added into an original cost function, a bound term in an original target function is removed, and solving of the optimum characteristic transition matrix is converted into solving of a matrix equation set. The algorithm is suitable for hyperspectral image characteristic extraction.

Description

technical field [0001] The invention belongs to the technical field of hyperspectral image data processing and application, and relates to a hyperspectral image feature extraction algorithm, in particular to a hyperspectral image feature extraction algorithm based on manifold learning linearization. Background technique [0002] Hyperspectral images are data cubes with a huge amount of information, and each pixel corresponds to a spectral line containing hundreds of bands, which provides the possibility for people to study the relationship between substances and spectral curves. However, there are data redundancy and dimensionality disaster problems in hyperspectral data, and there is an urgent need to eliminate the information redundancy of hyperspectral data. The redundancy of hyperspectral data is mainly caused by the correlation between the bands of hyperspectral data. Dimensionality reduction is an important preprocessing method, although linear methods such as PCA (Pri...

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