Indexed by:会议论文
Date of Publication:2018-01-01
Included Journals:CPCI-S
Volume:2018-July
Page Number:2643-2646
Key Words:hyperspectral image (HSI); convolutional neural networks (CNN); feature learning; supervised classification
Abstract:Convolutional neural networks (CNN), which are able to extract spatial semantic features, have achieved outstanding performance in many computer vision tasks. In this paper, hybrid neural networks (HNN) are proposed to extract both spatial and spectral features in the same deep networks. The proposed networks consist of different types of hidden layers, including spatial structure layer, spatial contextual layer, and spectral layer. All those layers work as organic networks to explore as much valuable information as possible from hyperspectral data for classification. Experimental results demonstrate competitive performance of the proposed approach over other state-of-the-art neural networks methods. Moreover, the proposed method is a new way to deal with multidimensional data with deep networks.
Associate Professor
Supervisor of Master's Candidates
Gender:Female
Alma Mater:Dalian University of Technology
Degree:Doctoral Degree
School/Department:School of Information and Communication Engineering
Discipline:Signal and Information Processing
Business Address:海山楼B513
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