A hyperspectral classification method based on attention constraint non-negative matrix factorization

Abstract

The invention relates to a hyperspectral classification method based on attention constraint non-negative matrix factorization. The method comprises the following steps: S1, inputting original hyperspectral image data; S2, normalizing the hyperspectral image matrix by using the highlight to obtain a to-be-processed data set X; S3, decomposing X by adopting NMF to obtain an end member matrix U anda bottom abundance matrix H; S4, normalizing the end member matrix U and the bottom abundance matrix H; S5, initializing the attention parameterization matrix W according to the bottom abundance matrix H; S6, normalizing the attention parameterization matrix W; S7, the hyperspectral image, the end element matrix and the bottom abundance matrix are subjected to attention parameterization matrix, and an attention non-negative matrix is adopted to be decomposed, updated and iterated to be converged to obtain an end element matrix and a corresponding abundance matrix; According to the invention, the end member position information in the abundance matrix obtained when the non-negative matrix decomposition technology decomposes the hyperspectral image is not easy to lose, so that the classification precision of the hyperspectral image is improved.

Description

technical field [0001] The present invention relates to the field of hyperspectral classification, and more specifically, to a hyperspectral classification method based on attention-constrained non-negative matrix factorization. Background technique [0002] Hyperspectral remote sensing technology is a remote sensing information acquisition technology developed on the basis of imaging spectroscopy; it can obtain hundreds of continuous high-resolution images on the spectrum, and each pixel in the image corresponds to a spectral curve. The dimension of the spectral information contained in it is equal to the number of imaging frames; since the bands of the hyperspectral image are dense and overlap between them, a continuous radiation curve can be used to represent the characteristics of each pixel in the image data, correspondingly, The spectral curve of a group of pixels can be used to represent the distribution of ground features; because of its high spectral resolution and ...

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Problems solved by technology

However, the ground reflectance spectral signal obtained by hyperspectral remote sensing is recorded in units of pixels, which is a synthesis of the spectral signals of surface materials corresponding to the pixel; if the pixel contains only one type of ground object, such as minerals, Water bodies, vegetation, etc., are called end members; if the pixel contains more than one type of surface features, it is called a mixed pixel; there will be a large number of "same objects with different spectra" and "different objects with the same spectrum" in hyperspectral images. Spectrum” phenomenon, purely using spectral information for classification is very likely to

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