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Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

Chen, Yushi and Jiang, Hanlu and Li, Chunyang and Jia, Xiuping and Ghamisi, Pedram (2016) Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 54 (10), pp. 6232-6251. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2016.2584107. ISSN 0196-2892.

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Official URL: http://ieeexplore.ieee.org/document/7514991/

Abstract

Due to the advantages of deep learning, in this paper, a regularized deep feature extraction (FE) method is presented for hyperspectral image (HSI) classification using a convolutional neural network (CNN). The proposed approach employs several convolutional and pooling layers to extract deep features from HSIs, which are nonlinear, discriminant, and invariant. These features are useful for image classification and target detection. Furthermore, in order to address the common issue of imbalance between high dimensionality and limited availability of training samples for the classification of HSI, a few strategies such as L2 regularization and dropout are investigated to avoid overfitting in class data modeling. More importantly, we propose a 3-D CNN-based FE model with combined regularization to extract effective spectral-spatial features of hyperspectral imagery. Finally, in order to further improve the performance, a virtual sample enhanced method is proposed. The proposed approaches are carried out on three widely used hyperspectral data sets: Indian Pines, University of Pavia, and Kennedy Space Center. The obtained results reveal that the proposed models with sparse constraints provide competitive results to state-of-the-art methods. In addition, the proposed deep FE opens a new window for further research.

Item URL in elib:https://elib.dlr.de/106352/
Document Type:Article
Title:Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Chen, YushiHarbin Institute of TechnologyUNSPECIFIED
Jiang, HanluHarbin Institute of TechnologyUNSPECIFIED
Li, ChunyangHarbin Institute of TechnologyUNSPECIFIED
Jia, XiupingUniversity of New South WalesUNSPECIFIED
Ghamisi, PedramDLR-IMF/TUM-LMFUNSPECIFIED
Date:October 2016
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:54
DOI :10.1109/TGRS.2016.2584107
Page Range:pp. 6232-6251
Editors:
EditorsEmailEditor's ORCID iD
Plaza, Antonio J.aplaza@unex.esUNSPECIFIED
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:—Convolutional neural network (CNN), deep learning, feature extraction (FE), hyperspectral image (HSI) classification.
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Ghamisi, Pedram
Deposited On:19 Oct 2016 09:59
Last Modified:31 Jul 2019 20:03

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