A hyperspectral image classification method based on a cooperative generation antagonistic network and a space spectrum combination

A hyperspectral image and classification method technology, applied in the field of hyperspectral image classification, can solve the problems of long network training time, misclassification, and small difference in spatial characteristics of spectral differences, so as to alleviate the phenomenon of network overfitting and improve accuracy Effect

Active Publication Date: 2019-01-04
XIDIAN UNIV
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Problems solved by technology

Although this method does not need to solve the complex global energy optimization problem, it can extract the non-local spatial information of the hyperspectral image to optimize the classification. For the problem of large and small differences in the spatial characteristics of different pixels, only extracting the spatial characteristics of the hyperspectral image will lead to a large number of misclassifications, resulting in low classification accuracy
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  • A hyperspectral image classification method based on a cooperative generation antagonistic network and a space spectrum combination
  • A hyperspectral image classification method based on a cooperative generation antagonistic network and a space spectrum combination
  • A hyperspectral image classification method based on a cooperative generation antagonistic network and a space spectrum combination

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Embodiment Construction

[0060] The present invention will be further described below in conjunction with the accompanying drawings.

[0061] Refer to attached figure 1 , to further describe the specific steps of the present invention.

[0062] Step 1. Obtain training sample set and test sample set.

[0063] Using principal component analysis method, the hyperspectral data is reduced in dimension.

[0064] The steps of the principal component analysis method are as follows.

[0065] In the first step, the 200-dimensional spectral channel of each pixel in the hyperspectral image matrix is ​​expanded into a 1×200 feature matrix.

[0066] The second step is to calculate the average value of the elements in the feature matrix by column, and subtract the mean value of the corresponding column of the feature matrix from each element in the feature matrix.

[0067] The third step is to calculate the covariance of every two columns of elements in the feature matrix, construct the covariance matrix of the ...

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Abstract

The invention discloses a hyperspectral image classification method based on a cooperative generation antagonistic network and a space spectrum combination. The method comprises the following steps: inputting a hyperspectral image; obtaining a sample set; generating training samples and test samples; building a multi-scale discriminator; constructing cooperative relationship; building a cooperative generation countermeasure network; the initial value of the training sample being generated by the multi-scale discriminator; the generator generating samples; a multi-scale discriminator perfromingclassification; constructing the loss function of generator and multiscale discriminator; alternatively training the generator and the multi-scale discriminator; hyperspectral images being classified. The invention utilizes the established cooperation to generate the antagonistic network, extracts the spatial spectrum combined characteristics of pixels, generates more realistic samples, increasesthe number of samples, alleviates the problems of over-fitting the network and slow convergence speed of the network, and improves the accuracy of the hyperspectral image classification.

Description

technical field [0001] The present invention belongs to the technical field of image processing, and further relates to a hyperspectral image classification method in the technical field of image classification through collaborative generative confrontation network and space-spectrum union. The invention can detect and recognize underwater obstacles and land tanks and warships in the collected hyperspectral images, and can analyze the types of crops in the hyperspectral images. Background technique [0002] Hyperspectral images can obtain approximately continuous spectral information of target ground objects in a large number of bands such as ultraviolet, visible light, near-infrared and mid-infrared, and describe the spatial distribution relationship of ground objects in the form of images, so as to establish "map-spectrum integration" data , can realize accurate identification and detail extraction of ground objects, and provide favorable conditions for understanding the o...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/2135G06F18/241
Inventor 冯婕冯雪亮陈建通焦李成张向荣王蓉芳刘若辰尚荣华
Owner XIDIAN UNIV
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