Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

This paper proposes a novel deep learning framework named bidirectional- convolutional long short term memory ( Bi- CLSTM) network to automatically learn the spectral- spatial features from hyperspectral images ( HSIs). In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network ( CNN), a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. In addition, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a Softmax classifier via a fully- connected operator. To validate the effectiveness of the proposed Bi- CLSTM framework, we compare it with six state- of- the- art methods, including the popular 3D- CNN model, on three widely used HSIs ( i. e., Indian Pines, Pavia University, and Kennedy Space Center). The obtained results show that Bi- CLSTM can improve the classification performance by almost 1.5% as compared to 3D- CNN.