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Proceedings Paper

Hyperspectral image feature extraction method based on sparse constraint convolutional neural network
Author(s): Peiyuan Jia; Miao Zhang; Wenbo Yu; Yi Shen
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Paper Abstract

According to the characters of complex hyperspectral data, sparsity technique is introduced to deep convolutional neural network to handle feature extraction and classification problems. Combining sparse unsupervised learning method with neural network model, it is possible to get a good, sparse representation of the spectral information so that deep CNN model could extract feature information hierarchically and effectively. EPLS algorithm is applied in this paper to combine population sparsity and lifetime sparsity with the advantages of extracting deep feature information of CNN model to get a fine classification model. In the experiment, two hyperspectral data sets are applied for the proposed method, and the results demonstrate fine classification performances of the model.

Paper Details

Date Published: 8 March 2017
PDF: 6 pages
Proc. SPIE 10255, Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016, 102552C (8 March 2017); doi: 10.1117/12.2268499
Show Author Affiliations
Peiyuan Jia, Harbin Institute of Technology (China)
Miao Zhang, Harbin Institute of Technology (China)
Wenbo Yu, Harbin Institute of Technology (China)
Yi Shen, Harbin Institute of Technology (China)


Published in SPIE Proceedings Vol. 10255:
Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016
Yueguang Lv; Jialing Le; Hesheng Chen; Jianyu Wang; Jianda Shao, Editor(s)

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