  Convolutional neural networks (CNNs) attained a good performance in
hyperspectral sensing image (HSI) classification, but CNNs consider spectra as
orderless vectors. Therefore, considering the spectra as sequences, recurrent
neural networks (RNNs) have been applied in HSI classification, for RNNs is
skilled at dealing with sequential data. However, for a long-sequence task,
RNNs is difficult for training and not as effective as we expected. Besides,
spatial contextual features are not considered in RNNs. In this study, we
propose a Shorten Spatial-spectral RNN with Parallel-GRU (St-SS-pGRU) for HSI
classification. A shorten RNN is more efficient and easier for training than
band-by-band RNN. By combining converlusion layer, the St-SSpGRU model
considers not only spectral but also spatial feature, which results in a better
performance. An architecture named parallel-GRU is also proposed and applied in
St-SS-pGRU. With this architecture, the model gets a better performance and is
more robust.
