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Open AccessArticle

Synergistic 2D/3D Convolutional Neural Network for Hyperspectral Image Classification

1
Harbin Institute of Technology, Shenzhen 518055, China
2
Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen 518055, China
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Department of Information Systems, City University of Hong Kong, Hong Kong 999077, China
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School of Computer Science and Engineering, Nanyang Technological University, Singapore 628798, Singapore
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School of Information Engineering, East China Jiaotong University, Nanchang 330000, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2020, 12(12), 2033; https://doi.org/10.3390/rs12122033
Received: 23 May 2020 / Revised: 13 June 2020 / Accepted: 15 June 2020 / Published: 24 June 2020
Accurate hyperspectral image classification has been an important yet challenging task for years. With the recent success of deep learning in various tasks, 2-dimensional (2D)/3-dimensional (3D) convolutional neural networks (CNNs) have been exploited to capture spectral or spatial information in hyperspectral images. On the other hand, few approaches make use of both spectral and spatial information simultaneously, which is critical to accurate hyperspectral image classification. This paper presents a novel Synergistic Convolutional Neural Network (SyCNN) for accurate hyperspectral image classification. The SyCNN consists of a hybrid module that combines 2D and 3D CNNs in feature learning and a data interaction module that fuses spectral and spatial hyperspectral information. Additionally, it introduces a 3D attention mechanism before the fully-connected layer which helps filter out interfering features and information effectively. Extensive experiments over three public benchmarking datasets show that our proposed SyCNNs clearly outperform state-of-the-art techniques that use 2D/3D CNNs. View Full-Text
Keywords: convolutional neural network; 3D CNN; hyperspectral image classification convolutional neural network; 3D CNN; hyperspectral image classification
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MDPI and ACS Style

Yang, X.; Zhang, X.; Ye, Y.; Lau, R.Y.K.; Lu, S.; Li, X.; Huang, X. Synergistic 2D/3D Convolutional Neural Network for Hyperspectral Image Classification. Remote Sens. 2020, 12, 2033. https://doi.org/10.3390/rs12122033

AMA Style

Yang X, Zhang X, Ye Y, Lau RYK, Lu S, Li X, Huang X. Synergistic 2D/3D Convolutional Neural Network for Hyperspectral Image Classification. Remote Sensing. 2020; 12(12):2033. https://doi.org/10.3390/rs12122033

Chicago/Turabian Style

Yang, Xiaofei; Zhang, Xiaofeng; Ye, Yunming; Lau, Raymond Y.K.; Lu, Shijian; Li, Xutao; Huang, Xiaohui. 2020. "Synergistic 2D/3D Convolutional Neural Network for Hyperspectral Image Classification" Remote Sens. 12, no. 12: 2033. https://doi.org/10.3390/rs12122033

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