Extracting method of optical spectrum vector cross-correlation features in hyper-spectral image classification

Abstract

The invention discloses an extracting method of optical spectrum vector cross-correlation features in hyper-spectral image classification. The method comprises the steps that pretreatment, normalization, denoising, dimensionality reduction and the like of hyper-spectral image data are included; boostrap sampling and weighted average are performed so as to obtain a reference sample set; spectral signal random process theoretical assumption includes first assumption and second assumption, wherein in the first assumption, spectral signals are random experiments of a stable random process at a certain time, and in the second assumption, the probabilities of all random experiment values are the equal, the spectral signals are abstracted according to the cross-correlation theory of the random process, a cross-correlation coefficient calculation formula is obtained, and finally cross-correlation feature vectors are formed by combination; sparse decomposition is carried out on the cross-correlation feature vectors through the MOD. The extracting method for features in the hyper-spectral classification is provided from the aspect of the cross-correlation of the random process, good noise immunity and high stability are achieved, and hyper-spectral classification precision is improved.

Description

technical field [0001] The invention belongs to the field of hyperspectral image processing, in particular to a method for extracting spectral vector cross-correlation features in hyperspectral image classification. Background technique [0002] The hyperspectral image organically integrates the traditional spatial dimension information and spectral dimension information. While acquiring the scene space image, it can obtain the continuous spectrum of all objects in the scene, so as to achieve the goal of classification and recognition based on the spectral characteristics of the object. Compared with traditional panchromatic and multi-spectral remote sensing, due to its high spectral resolution and spatial resolution, it can effectively combine spectral information and spatial information, and the amount of data is abundant, and the data model is easy to describe. Classification has outstanding advantages. With the development and maturity of hyperspectral imaging technolog...

AI Technical Summary

Problems solved by technology

The hyperspectral image itself has great defects, such as excessive data redundancy caused by massive amounts, spectral mixing caused by high spatial resolution, and the influence of noise, which greatly increases the difficulty of fine classification

The traditional hyperspectral feature matching classification method requires a lot of prior knowledge and is too dependent on the spectral feature database, while the statistical classification method is slow in operation and its accuracy is greatly affected by the training samples.

The existing feature extraction and classification methods are often limited by the defects of the hyperspectral image itself, which is manifested by the lack of stability and robustness of the algorithm

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