Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network

Haokui Zhang, Ying Li, Yuzhu Zhang, Qiang Shen
2017 Remote Sensing Letters  
In this paper, a novel dual-channel convolutional neural network (DC-CNN) framework is proposed for accurate spectral-spatial classification of hyperspectral image (HSI). In this framework, one-dimensional CNN (1D CNN) is utilized to automatically extract the hierarchical spectral features and two-dimensional CNN (2D CNN) is applied to extract the hierarchical space-related features, and then a softmax regression classifier is used to combine the spectral and spatial features together and
more » ... t classification results eventually. To overcome the problem of the limited available training samples in HSIs, we propose a simple data augmentation method which is e cient and e↵ective for improving HSI classification accuracy. For comparison and validation, we test the proposed method along with three other deep learning based HSI classification methods on two real-world HSI datasets. Experimental results demonstrate that our DC-CNN based method outperforms the state-of-the-art methods by a considerable margin.
doi:10.1080/2150704x.2017.1280200 fatcat:k2wivulba5fancunj6lobzdecm