By considering the spectral signature as a sequence, recurrent neural
networks (RNNs) have been successfully used to learn discriminative features
from hyperspectral images (HSIs) recently. However, most of these models only
input the whole spectral bands into RNNs directly, which may not fully explore
the specific properties of HSIs. In this paper, we propose a cascaded RNN model
using gated recurrent units (GRUs) to explore the redundant and complementary
information of HSIs. It mainly consists of two RNN layers. The first RNN layer
is used to eliminate redundant information between adjacent spectral bands,
while the second RNN layer aims to learn the complementary information from
non-adjacent spectral bands. To improve the discriminative ability of the
learned features, we design two strategies for the proposed model. Besides,
considering the rich spatial information contained in HSIs, we further extend
the proposed model to its spectral-spatial counterpart by incorporating some
convolutional layers. To test the effectiveness of our proposed models, we
conduct experiments on two widely used HSIs. The experimental results show that
our proposed models can achieve better results than the compared models
