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/ | ||||||||||||||||||
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Document Type: | Article | ||||||||||||||||||
Title: | Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks | ||||||||||||||||||
Authors: |
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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: |
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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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