Convolutional Recurrent Neural Networks for Hyperspectral Data Classification

Deep neural networks, such as convolutional neural networks (CNN) and stacked autoencoders, have recently been successfully used to extract deep features for hyperspectral data classification. Recurrent neural networks (RNN) are another type of neural networks, which are widely used for sequence analysis because they are constructed to extract contextual information from sequences by modeling the dependencies between different time steps. In this paper, we study the ability of RNN for hyperspectral data classification by extracting the contextual information from the data. Specifically, hyperspectral data are treated as spectral sequences, and an RNN is used to model the dependencies between different spectral bands. In addition, we propose to use a convolutional recurrent neural network (CRNN) to learn more discriminative features for hyperspectral data classification. In CRNN, a few convolutional layers are first learned to extract middle-level and locally-invariant features from the input data, and the following recurrent layers are then employed to further extract spectrally-contextual information from the features generated by the convolutional layers. Experimental results on real hyperspectral datasets show that our method provides better classification performance compared to traditional methods and other state-of-the-art deep learning methods for hyperspectral data classification.