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Deep Recurrent Neural Networks for Hyperspectral Image Classification

Mou, Lichao and Ghamisi, Pedram and Zhu, Xiaoxiang (2017) Deep Recurrent Neural Networks for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 55 (7), pp. 3639-3655. IEEE - Institute of Electrical and Electronics Engineers. ISSN 0196-2892

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Abstract

In recent years, vector-based machine learning algorithms, such as random forests, support vector machines, and 1-D convolutional neural networks, have shown promising results in hyperspectral image classification. Such methodologies, nevertheless, can lead to information loss in representing hyperspectral pixels, which intrinsically have a sequence-based data structure. A recurrent neural network (RNN), an important branch of the deep learning family, is mainly designed to handle sequential data. Can sequence-based RNN be an effective method of hyperspectral image classification? In this paper, we propose a novel RNN model that can effectively analyze hyperspectral pixels as sequential data and then determine information categories via network reasoning. As far as we know, this is the first time that an RNN framework has been proposed for hyperspectral image classification. Specifically, our RNN makes use of a newly proposed activation function, parametric rectified tanh (PRetanh), for hyperspectral sequential data analysis instead of the popular tanh or rectified linear unit. The proposed activation function makes it possible to use fairly high learning rates without the risk of divergence during the training procedure. Moreover, a modified gated recurrent unit, which uses PRetanh for hidden representation, is adopted to construct the recurrent layer in our network to efficiently process hyperspectral data and reduce the total number of parameters. Experimental results on three airborne hyperspectral images suggest competitive performance in the proposed mode. In addition, the proposed network architecture opens a new window for future research, showcasing the huge potential of deep recurrent networks for hyperspectral data analysis.

Item URL in elib:https://elib.dlr.de/114208/
Document Type:Article
Title:Deep Recurrent Neural Networks for Hyperspectral Image Classification
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Mou, Lichaolichao.mou (at) dlr.deUNSPECIFIED
Ghamisi, Pedrampedram.ghamisi (at) dlr.deUNSPECIFIED
Zhu, Xiaoxiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:2017
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:55
Page Range:pp. 3639-3655
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:Convolutional neural network (CNN), deep learning, gated recurrent unit (GRU), hyperspectral image classification, long short-term memory (LSTM), recurrent neural network (RNN)
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Mou, LiChao
Deposited On:19 Sep 2017 11:39
Last Modified:23 Feb 2019 00:21

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