Multi-Dimension CNN for Hyperspectral Image Classificaton

Hyperspectral remote sensing plays role in the field of earth observation research because of rich spatial, radiation and spectral information. With the rapid development of deep learning, deep neural networks are widely used in hyperspectral remote sensing image classification tasks, but at the same time, a series of difficulties have arisen, such as high demand for training samples, time-consuming model training. Convolutional neural network (CNN) is well known for its capability of feature learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, the one-dimension CNN (1D-CNN) modules was added after the two-dimension CNN (2D-CNN), so the complete network structure contains two different dimension CNN, called multi-dimension CNN (MD-CNN). The proposed 1D-CNN blocks can continue to learn contextual features and is expected to have more discriminative power. Experimental results with hyperspectral image benchmark datasets demonstrate that the proposed method can outperform the state-of-the-art CNN-based classification methods.