Hyperspectral remote sensing image classification method based on spatial regularization manifold learning algorithm

Abstract

The invention provides a hyperspectral remote sensing image dimension reduction and classification method based on spatial regularization manifold learning algorithm. The method comprises the following steps: the hyperspectral remote sensing image is divided into multiple sub blocks; partial data points are randomly selected to serve as connection data; the connection data and data of each sub block are combined to obtain enhanced sub block data; LLE algorithm and an image Laplacian matrix corresponding to regular spatial constraints are calculated respectively for each enhanced sub block, a composite Laplacian matrix is obtained, eigenvalue decomposition is carried out on the matrix, and a dimension reduction result is obtained; the dimension reductions are aligned, and a dimension reduction result for the overall image is obtained; and the dimension reduction data are classified finally. Data spatial information is effectively combined in a manifold learning algorithm framework, an image block and aligning strategy is adopted, and effects of regular spatial constraints can be achieved to the maximal degree. The algorithm is well adaptive to classification of multiple kinds of hyperspectral remote sensing data, and the classification precision of the hyperspectral remote sensing image can be improved obviously.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and specifically relates to a space-regularized manifold learning dimensionality reduction algorithm, combined with image block and dimensionality reduction result alignment technology, to maximize the role of space regular constraints, and to be used for hyperspectral remote sensing Image dimensionality reduction and classification. Background technique [0002] Hyperspectral remote sensing is a remote sensing science and technology with high spectral resolution. It has the characteristics of "integration of map and spectrum". Spectral and spatial information have important applications in the fine classification of ground objects. [0003] The amount of hyperspectral data is large, and there are problems of data redundancy and dimensionality disaster. Dimensionality reduction is an effective way to solve this problem. Dimensionality reduction can not only reduce the am...

AI Technical Summary

Problems solved by technology

Due to the high computational complexity of the semi-supervised classification algorithm, only a part of the data in the image can be selected to participate in the calculation.

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