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论文类型：会议论文

发表时间：2018-01-01

收录刊物：CPCI-S

卷号：2018-July

页面范围：2643-2646

关键字：hyperspectral image (HSI); convolutional neural networks (CNN); feature learning; supervised classification

摘要：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.