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Aurora image detection method based on deep learning two-dimensional principal component analysis network

A two-dimensional principal component, deep learning technology, applied in instrument, character and pattern recognition, scene recognition and other directions, can solve the problem of aurora image feature extraction, affecting the accurate classification of aurora images, etc., to improve the classification accuracy, realize computer automatic The effect of classification

Active Publication Date: 2018-04-17
XIDIAN UNIV
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Problems solved by technology

However, most of the existing studies on aurora images are based on the binary classification of arc and non-arc, and have not achieved good results in the three-classification of aurora images. The reason is that the features of aurora images are not fully extracted, so that Affected accurate classification of auroral images

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  • Aurora image detection method based on deep learning two-dimensional principal component analysis network
  • Aurora image detection method based on deep learning two-dimensional principal component analysis network

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Embodiment Construction

[0024] The content and effects of the present invention will be further described below in conjunction with the accompanying drawings.

[0025] Step 1: Extract the first-order global feature U of the aurora image.

[0026] The research object of this example is the aurora image, which is the aurora image captured by the all-sky camera of the Yellow River Station in the Arctic of my country at the Yellow River Station. The steps of extracting the first-order global features of the aurora image are as follows:

[0027] 1a) Extract the L of the aurora image 1 a first-order eigenvector

[0028] A variety of existing methods can be used to extract the first-order feature vector of the aurora image, such as two-dimensional principal component analysis, principal component analysis and other methods. In this example, the two-dimensional principal component analysis method is used to extract the L of the aurora image. 1 a first-order eigenvector Proceed as follows:

[0029] 1a...

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Abstract

The invention discloses an aurora image detection method based on deep learning two-dimensional principal component analysis, which mainly solves the problem of insufficient extraction of aurora image information in the prior art. The implementation steps are: (1) use the two-dimensional principal component analysis method to extract the first-order feature vector and generate the first-order filter matrix to obtain the first-order global feature; (2) extract the second-order feature vector of the first-order global feature, and at the same time Generate a second-order filter matrix to obtain second-order global features; (3) perform block histogram statistics on second-order global features to extract block histogram features; (4) use support vector machine classifier to block histogram The features are classified and the classification results are obtained. The invention realizes computer automatic detection of the existing three types of aurora images, has the advantages of high classification accuracy, and can be used for feature extraction and computer image detection of aurora images.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to an aurora image detection method, and can be used for feature extraction and classification of aurora images. Background technique [0002] Aurora is an atmospheric luminescence phenomenon excited by high-energy particles from the magnetosphere settling into the upper atmosphere and colliding with neutral components. Therefore, people can obtain a large amount of information on the magnetosphere and solar-terrestrial space electromagnetic activities through the systematic observation of the shape of the aurora and its evolution, which is helpful for in-depth research on the influence of solar activities on the earth. significance. As an important form in nature, aurora images reveal geomagnetic activity and pulsation phenomena in the magnetosphere through their distribution and occurrence. The research on the classification of aurora images has reached a level that is diffic...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/13G06F18/213G06F18/2411
Inventor 韩冰贾中华高新波李洁宋亚婷王平王秀美王颖
Owner XIDIAN UNIV
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