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/
Abstract
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/ | |||||||||||||||||||||
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Document Type: | Article | |||||||||||||||||||||
Title: | Hyperspectral Images Classification With Gabor Filtering and Convolutional Neural Network | |||||||||||||||||||||
Authors: |
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Date: | December 2017 | |||||||||||||||||||||
Journal or Publication Title: | IEEE Geoscience and Remote Sensing Letters | |||||||||||||||||||||
Refereed publication: | Yes | |||||||||||||||||||||
Open Access: | No | |||||||||||||||||||||
Gold Open Access: | No | |||||||||||||||||||||
In SCOPUS: | Yes | |||||||||||||||||||||
In ISI Web of Science: | Yes | |||||||||||||||||||||
Volume: | 14 | |||||||||||||||||||||
DOI : | 10.1109/LGRS.2017.2764915 | |||||||||||||||||||||
Page Range: | pp. 2355-2359 | |||||||||||||||||||||
Publisher: | IEEE - Institute of Electrical and Electronics Engineers | |||||||||||||||||||||
ISSN: | 1545-598X | |||||||||||||||||||||
Status: | Published | |||||||||||||||||||||
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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