Convolutional Recurrent Neural Networks for
Hyperspectral Data Classification
AbstractDeep 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.
Share & Cite This Article
Wu, H.; Prasad, S. Convolutional Recurrent Neural Networks for
Hyperspectral Data Classification. Remote Sens. 2017, 9, 298.
Wu H, Prasad S. Convolutional Recurrent Neural Networks for
Hyperspectral Data Classification. Remote Sensing. 2017; 9(3):298.
Wu, Hao; Prasad, Saurabh. 2017. "Convolutional Recurrent Neural Networks for
Hyperspectral Data Classification." Remote Sens. 9, no. 3: 298.
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