Convolutional neural network based classification for hyperspectral data

A novel deep learning classification method for hyperspectral data based on convolutional neural network is proposed in this paper. Deep learning means bringing multiple layers instead of one to the structure. Through convolution layers and pooling layers, the features in different layers are extracted from original spectral feature images. The key of this method is to restructure spectral feature images and choose convolution filters with a reasonable size, so that the spectral features of different land coverings in high dimensions can be extracted properly. In our experiments, proposed method was applied for hyperspectral data in several different situations, and preferable classification performance were obtained through relative parameters adjustment, which were given recommended scope during our comparative experiments.