High-spectral unsupervised classification method for constructing generic dictionary based on confidence degrees

Abstract

The invention discloses a high-spectral unsupervised classification method for constructing a generic dictionary based on confidence degrees. The method comprises the steps of firstly, constructing a two-dimensional spectral-pixel matrix; performing row and column standardization processing; performing feature extraction and selection to obtain dimension reduction features of pixels; performing coarse classification and confidence degree assessment, namely, classifying the pixels by utilizing the dimension reduction features, calculating Euclidean distances between the spectral pixels and a coarse classification category center to serve as the confidence degrees, and obtaining high-confidence-degree classified samples and low-confidence-degree classified samples; and finally, performing secondary classification based on kernel sparse representation, namely, forming the generic dictionary by the high-confidence-degree classified samples, performing the kernel sparse representation on the low-confidence-degree classified samples, and determining classification tags of the low-confidence-degree spectral pixels. According to the method, the problems of low classification sub-space description precision and excessively high computing complexity due to construction of the dictionary by directly utilizing all spectral data are solved; the dictionary sub-space identification performance is improved; and the misclassification error rate is reduced.

Description

technical field [0001] The invention belongs to the unsupervised technical field of hyperspectral images, in particular to a hyperspectral unsupervised classification method for constructing a category dictionary based on confidence. Background technique [0002] Hyperspectral image data is decomposed according to dozens or even hundreds of continuous narrow-band wavelengths in the spectral interval, which has rich spectral feature information of ground features and can be widely used in fine classification of ground features, mineral surveys and other fields. How to use the massive data and high-dimensional characteristics of hyperspectral images to combine various features of hyperspectral images to study fast and efficient target recognition and classification algorithms has always been a hot spot in the research of hyperspectral image processing. [0003] For hyperspectral image unsupervised classification methods, classical methods often have low classification accuracy...

AI Technical Summary

Problems solved by technology

By using the original data samples of the image, some or all of the spectral pixels are used to construct a sparsely represented dictionary. Although this method is simple in construction and low in computational complexity, the discrimination of the sparse subspace is insufficient, which leads to this method constructing a dictionary for sparse representation. The misclassification rate is relatively high

The method of dictionary learning, the representation dictionary obtained through sample learning can better match the structure of the image itself, and has a sparser representation, but the computational complexity is high

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