Random Convolutional Network for Hyperspectral Image Classification

Zhu, Jiongye; Wang, Xiaohan; Lei, Ling; Ye, Minchao; Qian, Yuntao

Convolutional neural network (CNN) has proved remarkable performance in the field of hyperspectral image (HSI) classification for it has excellent feature extraction ability. However, HSI classification is a small-sample-size problem due to the labour cost of labeling. CNN may perform poorly on HSI data due to the ill-conditioned and overfitting problems caused by the lack of enough training samples. Extreme learning machine (ELM) is a kind of single-layer feedforward neural network (FNN) with high training efficiency, which simplifies the learning of parameters. Therefore, in this paper, we try to combine the convolutional feature extraction method of CNN and the parameter randomization idea of ELM, and then propose a random convolutional network (RCN) model. The proposed RCN randomly generates the parameters of three-dimensional (3D) convolution kernels in convolutional layer used for the joint spectral-spatial feature extraction. RCN avoids ill-conditioned and overfitting problems in the case of small samples by significantly reducing the number of parameters to be trained. At the same time, further analyses on the convolution kernel sizes and the number of convolution kernels have been carried out. Experiments on two real-world HSI datasets have demonstrated that the proposed RCN algorithm has excellent generalization ability.