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Article

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

Jiangsu Key Laboratory of Big Data Analysis Technology, School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China
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Remote Sens. 2017, 9(12), 1330; https://doi.org/10.3390/rs9121330
Received: 12 November 2017 / Revised: 2 December 2017 / Accepted: 14 December 2017 / Published: 19 December 2017
(This article belongs to the Section Remote Sensing Image Processing)
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. View Full-Text
Keywords: feature learning; long short term memory; convolution operator; bidirectional recurrent network; hyperspectral image classification feature learning; long short term memory; convolution operator; bidirectional recurrent network; hyperspectral image classification
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MDPI and ACS Style

Liu, Q.; Zhou, F.; Hang, R.; Yuan, X. Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification. Remote Sens. 2017, 9, 1330. https://doi.org/10.3390/rs9121330

AMA Style

Liu Q, Zhou F, Hang R, Yuan X. Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification. Remote Sensing. 2017; 9(12):1330. https://doi.org/10.3390/rs9121330

Chicago/Turabian Style

Liu, Qingshan; Zhou, Feng; Hang, Renlong; Yuan, Xiaotong. 2017. "Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification" Remote Sens. 9, no. 12: 1330. https://doi.org/10.3390/rs9121330

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