8 March 2017 Hyperspectral image feature extraction method based on sparse constraint convolutional neural network
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Proceedings Volume 10255, Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016; 102552C (2017) https://doi.org/10.1117/12.2268499
Event: Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016, 2016, Jinhua, Suzhou, Chengdu, Xi'an, Wuxi, China
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.
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Peiyuan Jia, Peiyuan Jia, Miao Zhang, Miao Zhang, Wenbo Yu, Wenbo Yu, Yi Shen, Yi Shen, } "Hyperspectral image feature extraction method based on sparse constraint convolutional neural network", 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; https://doi.org/10.1117/12.2268499
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