Dual-channel convolutional neural network-based spectral-spatial cooperative classification method for hyperspectral images

A convolutional neural network and hyperspectral image technology, applied in the field of space-spectrum joint hyperspectral image classification, can solve problems such as data overfitting, expand the application range, reduce overfitting problems, and improve classification accuracy Effect

Active Publication Date: 2017-06-13
NORTHWESTERN POLYTECHNICAL UNIV
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At the same time, a data expansion method is proposed to overcome the problem

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  • Dual-channel convolutional neural network-based spectral-spatial cooperative classification method for hyperspectral images
  • Dual-channel convolutional neural network-based spectral-spatial cooperative classification method for hyperspectral images
  • Dual-channel convolutional neural network-based spectral-spatial cooperative classification method for hyperspectral images

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Embodiment Construction

[0033] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0034] Step 1: Input hyperspectral image data, according to the formula Normalize the data. where x ijs Represents a pixel in the hyperspectral image, i and j respectively represent the coordinate position of the pixel in the hyperspectral image, s represents the spectral segment of the hyperspectral image, and the existing hyperspectral image generally contains 100-240 spectral segments , x ··smax 、x ··smin Respectively represent the maximum and minimum values ​​of the three-dimensional hyperspectral image in the s-band.

[0035] Step 2 extracts the original spectral data samples and spatial data samples. For each pixel to be classified, extract all the information within the eight neighborhoods as the spectral data sample of the pixel L represents the total number of spectral segments. Compress the data through PCA dimensionality reduction, retain the ...

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Abstract

The invention relates to a dual-channel convolutional neural network (DC-CNN)-based spectral-spatial cooperative classification method for hyperspectral images. For a characteristic that hyperspectral image data adopts a three-dimensional structure, spectral-spatial features are extracted in a mode of combining a one-dimensional convolutional neural network (1D-CNN) channel with a two-dimensional convolutional neural network (2D-CNN) channel to finish the spectral-spatial cooperative classification of the hyperspectral images. For the problem of relatively little artificial mark data of the hyperspectral images, by adopting a data expansion method suitable for the hyperspectral images, the scale of training samples is increased, the training efficiency of a CNN is improved, and the over-fitting problem is reduced.

Description

technical field [0001] The invention belongs to the technical field of remote sensing information processing, and relates to a hyperspectral image classification method, in particular to a hyperspectral image classification method based on dual-channel convolutional neural network space-spectrum union. Background technique [0002] Hyperspectral remote sensing images have high spectral resolution, multiple imaging bands, and large amounts of information, and are widely used in remote sensing applications. Hyperspectral image classification technology is an important part of hyperspectral image processing technology. It mainly includes two steps of feature extraction and classification. Among them, features are extracted from the original hyperspectral image. This step has a great impact on the classification accuracy of hyperspectral images: if The robustness of the extracted classification features can greatly improve the classification accuracy; on the contrary, the classi...

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Application Information

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IPC IPC(8): G06K9/00G06N3/08
CPCG06N3/084G06V20/194G06V20/13
Inventor 李映张号逵其他发明人请求不公开姓名
Owner NORTHWESTERN POLYTECHNICAL UNIV
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