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Hyperspectral Images Classification With Gabor Filtering and Convolutional Neural Network

Chen, Yushi and Zhu, Lin and Ghamisi, Pedram and Jia, Xiuping and Li, Guoyu and Tang, Liang (2017) Hyperspectral Images Classification With Gabor Filtering and Convolutional Neural Network. IEEE Geoscience and Remote Sensing Letters, 14 (12), pp. 2355-2359. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2017.2764915. ISSN 1545-598X.

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Official URL: http://ieeexplore.ieee.org/document/8100719/


Recently, the capability of deep learning-based approaches, especially deep convolutional neural networks (CNNs), has been investigated for hyperspectral remote sensing feature extraction (FE) and classification. Due to the large number of learnable parameters in convolutional filters, lots of training samples are needed in deep CNNs to avoid the overfitting problem. On the other hand, Gabor filtering can effectively extract spatial information including edges and textures, which may reduce the FE burden of the CNNs. In this letter, in order to make the most of deep CNN and Gabor filtering, a new strategy, which combines Gabor filters with convolutional filters, is proposed for hyperspectral image classification to mitigate the problem of overfitting. The obtained results reveal that the proposed model provides competitive results in terms of classification accuracy, especially when only a limited number of training samples are available.

Item URL in elib:https://elib.dlr.de/118212/
Document Type:Article
Title:Hyperspectral Images Classification With Gabor Filtering and Convolutional Neural Network
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Chen, YushiHarbin Institute of TechnologyUNSPECIFIED
Zhu, LinHarbin Institute of TechnologyUNSPECIFIED
Ghamisi, PedramPedram.Ghamisi (at) dlr.deUNSPECIFIED
Jia, XiupingUniversity of New South WalesUNSPECIFIED
Li, GuoyuChinese Acadamy of Sciences, LhanzouUNSPECIFIED
Tang, LiangHarbin Institute of TechnologyUNSPECIFIED
Date:December 2017
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/LGRS.2017.2764915
Page Range:pp. 2355-2359
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Convolutional Neural Networks (CNNs), feature extraction (FE), Gabor Filtering
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 - Optical remote sensing, R - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Zielske, Mandy
Deposited On:12 Jan 2018 15:13
Last Modified:08 Mar 2018 18:31

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