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Remote Sens. 2018, 10(5), 783;

Deep Cube-Pair Network for Hyperspectral Imagery Classification

School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
School of Electronic and Engineering, Xidian University, Xi’an 710071, China
Authors to whom correspondence should be addressed.
Received: 17 March 2018 / Revised: 23 April 2018 / Accepted: 16 May 2018 / Published: 18 May 2018
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Advanced classification methods, which can fully utilize the 3D characteristic of hyperspectral image (HSI) and generalize well to the test data given only limited labeled training samples (i.e., small training dataset), have long been the research objective for HSI classification problem. Witnessing the success of deep-learning-based methods, a cube-pair-based convolutional neural networks (CNN) classification architecture is proposed to cope this objective in this study, where cube-pair is used to address the small training dataset problem as well as preserve the 3D local structure of HSI data. Within this architecture, a 3D fully convolutional network is further modeled, which has less parameters compared with traditional CNN. Provided the same amount of training samples, the modeled network can go deeper than traditional CNN and thus has superior generalization ability. Experimental results on several HSI datasets demonstrate that the proposed method has superior classification results compared with other state-of-the-art competing methods. View Full-Text
Keywords: hyperspectral imagery; convolutional neural network; deep learning; datacube; spatial-spectral hyperspectral imagery; convolutional neural network; deep learning; datacube; spatial-spectral

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Wei, W.; Zhang, J.; Zhang, L.; Tian, C.; Zhang, Y. Deep Cube-Pair Network for Hyperspectral Imagery Classification. Remote Sens. 2018, 10, 783.

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