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

G. Fu, H. Gu

in The 7th International Conference on Computer Engineering and Networks

Contribution: pdf


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
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