Automatic Design Recurrent Neural Network for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is a hot research direction in remote sensing community. In HSIs, there are hundreds of narrow and continuous spectral bands. Due to powerful sequence data processing ability, recurrent neural network (RNN) has shown great potential in HSI classification in recent years. In order to ensure a good classification performance, how to design a proper RNN structure is of great importance. In this paper, an automatic RNN (Auto-RNN) for HSI classification is proposed. Firstly, a number of candidate modules, including ReLU, tanh, sigmoid, and identity, are provided. Then, a policy gradient-based reinforcement learning is us ed to search the feasible deep architecture. Finally, the best RNN structure is selected through evaluating on the validation set. The experimental results demonstrate that the proposed algorithm can yield promising classification performance compared with some existing methods.