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Optimized convolution automatic encoding network-based auroral image sorting method

An automatic encoding and classification method technology, applied in the field of image processing, can solve the problems of no processing method, low accuracy of aurora image classification, and serious time-consuming training of deep convolutional network

Active Publication Date: 2016-05-04
XIDIAN UNIV +1
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Problems solved by technology

[0005] However, there are still several problems in using the deep convolutional network directly for the feature extraction of aurora images: firstly, because there are many completely black parts in the aurora images without any information at all, the existing deep learning algorithms have no redundancy for this part. Processing method; secondly, due to the limitation of the number of training samples, the classification accuracy of the existing deep convolutional network technology for aurora images is not high; thirdly, the training of the deep convolutional network is time-consuming

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[0031] The implementation steps and technical effects of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0032] refer to figure 1 , the implementation steps of the present invention are as follows:

[0033] Step 1, input the aurora image, and extract the training pixel block set P 8×8×100000 .

[0034] 1.1) Enter a picture such as figure 2 For the aurora image shown in (a), obtain the brightness feature L(x,y), gradient feature H(x,y) and edge binarization feature B of each pixel point I(x,y) in the image (x, y), and these three features are fused to obtain the saliency information value S(x, y) of the pixel point I(x, y) of the aurora image:

[0035] S(x,y)=L(x,y)+H(x,y)+B(x,y);

[0036] The saliency information value S(x,y) of all points in the aurora image is composed as figure 2 (b) Salient map S of the auroral image shown;

[0037] 1.2) Binarize the image saliency map S to obtain the following fig...

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Abstract

The invention discloses an optimized convolution automatic encoding network-based auroral image sorting method, and mainly aims at solving the problem that the existing technology is relatively low in auroral image sorting accuracy. The method comprises the following realization steps: 1, solving a saliency map of auroral images and extracting training samples on the basis of the saliency map; 2, carrying out pre-whitening on the training samples; 3, training an automatic encoding network AE; 4, solving the convolution self-encoding characteristics of the auroral images by utilizing the trained automatic encoding network; 5, carrying out mean pooling on the convolution self-encoding characteristics of the auroral images; and 6, inputting the pooled convolution self-encoding characteristics into a softmax sorter so as to sort the auroral images.

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 of images. Background technique [0002] The aurora is the most intuitive trace of the ionosphere in various magnetosphere dynamic processes. The all-sky imaging system All-skyCamera of the Yellow River Station in the North Pole of China simultaneously continuously observes the three typical spectral bands of the aurora, 427.8nm, 557.7nm and 630.0nm, and produces There are tens of thousands of aurora images, and the amount of data is huge. Reasonable and effective classification of auroral images 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...

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Application Information

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IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/088G06F18/2413G06F18/214
Inventor 韩冰胡泽骏宋亚婷高新波胡红桥贾中华褚福跃李洁
Owner XIDIAN UNIV
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