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For the classification of hyperspectral images, a classification algorithm based on band grouping and three-dimensional convolutional neural network(3D-CNN-BG) is proposed. The algorithm uses the correlation matrix of hyperspectral images to determine the similarity of bands, and the high similarity bands are  grouped together. Then, every bands group is extracted spatial-spectral feature using 3D convolutional neural network. Finally, the high-level feature of every 3D-CNN is stacked together trained by the classifier. With band regroup, the feature in the similar group can be extracted fully by the 3D-CNN, which is good for classification. The experiments showed that compared with other hyperspectral images classification algorithms based on CNN, the proposed algorithm converges more faster, has less training parameters, and acquires higher classification accuracy. After training 100 times, the overall classification accuracy on the indian pines data set can reach 97.42%, increased by approximately 2% ~ 5%, which shows the strong practicability of proposed algorithm in hyperspectral application

The Classification of Hyperspectral Images Based on Band-Grouping and Convolutional Neural Network