Aurora image classification method based on biological stimulation characteristic and manifold learning

Abstract

The invention discloses an aurora image classification method based on biological stimulation characteristics and manifold learning. The method comprises the following steps of: (1) carrying out preprocessing of edge denoising on an input aurora image; (2) carrying out Gabor filtering on the aurora image subjected to preprocessing by using a multi-directional Gabor filter group, so as to obtain C1-layer characteristic graphs, and taking the sum of pixel gray level values of each characteristic graph as a C1 characteristic of the aurora image; (3) extracting a Gist characteristic of the aurora image; (4) fusing the C1 characteristic and the Gist characteristic so as to obtain a BIFs characteristic of the aurora image; (5) carrying out fuzzy C-mean value clustering on the BIFs characteristic, and subsequently carrying out dimensionality reduction by using a manifold learning algorithm so as to obtain the expression of the BIFs in a low-dimension space; and (6) respectively classifying aurora images by using a support vector machine (SVM) and a nearest neighbor (NN) classifier. By utilizing the method, the recognition process of human brain visual cortex can be well simulated, data redundancy is reduced, classification accuracy rate is improved, and therefore the method can be used for scene classification and object recognition.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a classification method of aurora images, which can be used for scene classification and target recognition. Background technique [0002] Image classification is one of the key technologies in image processing and pattern recognition. It uses computers to quantitatively analyze images, and distinguishes different types of targets according to different features reflected in image information, thereby replacing human vision. Interpretation. The aurora is the most intuitive trace of the ionosphere for various dynamic processes of the magnetosphere. Reasonable and effective classification of aurora is particularly important for the study of various auroral phenomena and their relationship with the dynamic process of the magnetosphere. [0003] Early research on aurora classification was based on naked eye observation, and manual marking and classification were carri...

AI Technical Summary

Problems solved by technology

In 2009, Fu et al. put the morphology Combining component analysis with MCA and aurora image processing, features are extracted from the aurora texture submap obtained after MCA separation, which is used to classify two types of auroral images of the arc crown, which improves the accuracy of the arc crown aurora classification; in 2010, Wang in the literature "WangY, Gao X., FuR., et al., Dayside Corona Aurora Classification Based on X-Gray Level Aura Matrices.Proc.ACM Int.Conf.Image and Video Retrieval, 282-287, 2010." According to the characteristics of aurora shape, the neighborhood shape of GLAM is improved, and a day-side aurora image classification algorithm based on X-GLAM features is proposed, which greatly improves the classification accuracy. However, due to the large-scale pixel operation of this method, thus more time consuming

[0006] In summary, none of the existing aurora image classification methods nor the human brain perception model of current psychological research can effectively achieve aurora image classification

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