Space spectrum full convolution hyperspectral image classification method based on superpixel sample expansion

A hyperspectral image and sample expansion technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve the problem of high cost of hyperspectral image labeling, achieve stable and reliable classification results, avoid highly redundant training data, Enhanced Robustness Effects

Active Publication Date: 2020-09-22
XIDIAN UNIV
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However, since the training of convolutional neural networks requires a large number of labeled samples as training samples, and t

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  • Space spectrum full convolution hyperspectral image classification method based on superpixel sample expansion
  • Space spectrum full convolution hyperspectral image classification method based on superpixel sample expansion
  • Space spectrum full convolution hyperspectral image classification method based on superpixel sample expansion

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[0055] The invention provides a space-spectrum full-convolution hyperspectral image classification method based on superpixel sample expansion, input hyperspectral images; obtain training sets and test sets; perform principal component analysis and dimensionality reduction on hyperspectral images; dimensionality reduction As a result, perform entropy superpixel segmentation; generate pseudo-label samples; update the training set; perform data preprocessing on hyperspectral images; input fully convolutional neural networks; train fully convolutional neural networks to classify hyperspectral images; repeat the above operations And vote; output hyperspectral classification results. The present invention uses entropy rate superpixel segmentation results to expand pseudo-label samples, fully utilizes the spatial prior information of hyperspectral images, increases the number of samples, alleviates the problem of network over-fitting, and effectively improves high-resolution images u...

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Abstract

The invention discloses a space-spectrum full-convolution hyperspectral image classification method based on superpixel sample expansion. The method comprises the following steps: inputting a hyperspectral image; obtaining a training set and a test set; performing principal component analysis and dimension reduction on the hyperspectral image; performing entropy rate segmentation on the dimensionreduction result; generating a pseudo label sample; updating the training set; performing data preprocessing on the hyperspectral image; inputting the data into a convolutional neural network; training a convolutional neural network, and classifying the hyperspectral images; repeating the above operations and performing voting; and outputting a high spectral classification result. According to themethod, the entropy rate super-pixel segmentation result is used for expanding the pseudo-label sample, the prior characteristics of the hyperspectral image are fully used, the number of samples is increased, the problems of network overfitting and low network convergence speed are solved, and the classification accuracy of the hyperspectral image under the condition of scarcity of marked samplesis improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for classifying hyperspectral images with spatial spectrum full convolution based on superpixel sample expansion. Background technique [0002] With the advancement of science and technology, hyperspectral remote sensing technology has been greatly developed. Hyperspectral data can be represented as a hyperspectral data cube, which is a three-dimensional data structure. Hyperspectral data can be regarded as a three-dimensional image, and one-dimensional spectral information is added to the ordinary two-dimensional image. Its spatial image describes the two-dimensional spatial characteristics of the earth's surface, and its spectral dimension reveals the spectral curve characteristics of each pixel in the image, thus realizing the organic fusion of remote sensing data spatial dimension and spectral dimension information. Hyperspectral remote sensing...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/13G06V20/194G06V10/464G06N3/045G06F18/2415Y02A40/10
Inventor 王佳宁李林昊郭思颖黄润虎杨攀泉焦李成侯彪张向荣毛莎莎
Owner XIDIAN UNIV
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