Hyperspectral image classification based on hybrid convolutional neural network

The traditional convolutional neural network method can loss some feature information, which may lead to unsatisfied terrain classification accuracy in the field of hyperspectral. In order to solve the problem, a new hyperspectral images classification method based on the 2-D and the 3-D, named hybrid convolutional neural network, was proposed. This method mainly extracted features from the spatial enhancement aspect and the spectral-spatial aspect. Firstly, a 3-D-2-D convolutional neural network hybrid structure was proposed for enhance spatial information. Secondly, the 3-D convolutional neural network structure was used for joint feature extraction from the aspect of spectral-spatial, and then the spectral-spatial comprehensive separability information was obtained. Finally, the separately obtained information was feature fused and classified. This method was used for classification experiments on hyperspectral data sets and compared with other methods. The results show that the classification accuracy of this method is 99.36% and 99.95% respectively in Indian Pines and Pavia University data set, and its classification accuracy and kappa coefficient are also better than other methods. This method has a competitive advantage in the classification of hyperspectral images.