Sparse self-adaptive semi-supervised manifold learning hyperspectral image classification method

Abstract

The invention provides a sparse self-adaptive semi-supervised manifold learning hyperspectral image classification method and provides a semi-supervised sparse manifold learning dimension reduction algorithm and a nearest manifold classification algorithm. Internal attributes and a manifold structure contained in high-dimensional data are well discovered by marking small amount of data points in a data sample and combining part of unmarked data points for learning, low-dimension embedded features with better identification performance can be extracted, classification result is improved, classification precision of land feature classification in hyperspectral remote sending images is improved, and problems about 'out-of-sample learning' of a sparse manifold clustering and embedding algorithm and difficulty in labeling the remote sensing image classification can be effectively solved; meanwhile, as is shown in experimental results in a PaviaU data set and compared with a common identification method in the prior art, the sparse self-adaptive semi-supervised manifold learning hyperspectral image classification method is better in classification result.

Description

technical field [0001] The invention relates to the technical field of hyperspectral data processing methods and applications, in particular to a hyperspectral image classification method for sparse adaptive semi-supervised multi-manifold learning. Background technique [0002] Hyperspectral remote sensing technology has developed rapidly since the 1980s. Its images record the continuous spectrum of ground objects, contain richer information, and have the ability to identify more types of ground objects and classify objects with higher accuracy. However, since hyperspectral data consists of a large number of bands to form a high-dimensional feature space, the complexity of most algorithms increases exponentially with the number of dimensions, and its processing requires a greater amount of calculation, and its bands are highly correlated and redundant. , at the same time, there are problems such as high dimensionality, easy to obtain ideal results due to Hughes phenomenon du...

AI Technical Summary

Problems solved by technology

Elhamifar et al. proposed a Sparse Manifold Clustering and Embedding (SMCE) algorithm, which can adaptively select data from the same manifold, and these data points from the same manifold span the same Low-dimensional affine subspace, the similarity graph constructed on this basis can better reveal the intrinsic characteristics of different manifolds in the data, and has a good effect in data clustering, but this method is only defined in the training samples , new samples cannot be directly obtained, and there is a problem of "out-of-sample learning", and this method does not make full use of the category information of the training samples, which limits the identification ability of the algorithm

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