A new hyperspectral band selection approach based on convolutional neural network

Band selection is a very important hyperspectral image preprocessing before using data. A novel bands selection method for hyperspectral data based on convolutional neural network (CNN) is proposed in this paper. In this way, we use a custom one-dimensional CNN to train the hyperspectral data to obtain a well-trained model. After testing band combinations, we use the model to obtain the test precision of the different band combinations, and finally use the band combination with the highest precision as the selected bands. This precision measure is a new criterion for band selection. This is the first application of CNN to band selection, and our proposed method can select the better combinations of band for specific problems. In the experiments, we select the bands on the Indian Pines dataset. The experimental results show that the proposed method can acquire satisfactory results when compared with traditional methods.