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Remote Sens. 2017, 9(12), 1330; doi:10.3390/rs9121330

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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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)
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Abstract

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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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.

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