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 predict 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.
In REF , Zhang et al. proposed a dual-channel convolutional neural network-based spectral-spatial classification framework; in this article, a one-dimensional CNN is applied to extract the hierarchical spectral features and a two-dimensional CNN is used to extract the hierarchical spatial features, then a softmax regression classifier is used to combine the spectral and spatial features together and predict classification results.
Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network
Remote Sensing Letters
Computer Science