Chen, Yushi and Li, Chunyang and Ghamisi, Pedram and Jia, Xiuping and Gu, Yanfeng (2017) Deep Fusion of Remote Sensing Data for Accurate Classification. IEEE Geoscience and Remote Sensing Letters, 14 (8), pp. 1253-1257. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2017.2704625. ISSN 1545-598X.
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Official URL: http://ieeexplore.ieee.org/document/7940007/
Abstract
The multisensory fusion of remote sensing data has obtained a great attention in recent years. In this letter, we propose a new feature fusion framework based on deep neural networks (DNNs). The proposed framework employs deep convolutional neural networks (CNNs) to effectively extract features of multi-/hyperspectral and light detection and ranging data. Then, a fully connected DNN is designed to fuse the heterogeneous features obtained by the previous CNNs. Through the aforementioned deep networks, one can extract the discriminant and invariant features of remote sensing data, which are useful for further processing. At last, logistic regression is used to produce the final classification results. Dropout and batch normalization strategies are adopted in the deep fusion framework to further improve classification accuracy. The obtained results reveal that the proposed deep fusion model provides competitive results in terms of classification accuracy. Furthermore, the proposed deep learning idea opens a new window for future remote sensing data fusion.
Item URL in elib: | https://elib.dlr.de/112797/ | ||||||||||||||||||
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Document Type: | Article | ||||||||||||||||||
Title: | Deep Fusion of Remote Sensing Data for Accurate Classification | ||||||||||||||||||
Authors: |
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Date: | August 2017 | ||||||||||||||||||
Journal or Publication Title: | IEEE Geoscience and Remote Sensing Letters | ||||||||||||||||||
Refereed publication: | Yes | ||||||||||||||||||
Open Access: | Yes | ||||||||||||||||||
Gold Open Access: | No | ||||||||||||||||||
In SCOPUS: | Yes | ||||||||||||||||||
In ISI Web of Science: | Yes | ||||||||||||||||||
Volume: | 14 | ||||||||||||||||||
DOI : | 10.1109/LGRS.2017.2704625 | ||||||||||||||||||
Page Range: | pp. 1253-1257 | ||||||||||||||||||
Editors: |
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Publisher: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||||
ISSN: | 1545-598X | ||||||||||||||||||
Status: | Published | ||||||||||||||||||
Keywords: | Convolutional neural network (CNN), data fusion, deep neural network (DNN), feature extraction (FE), multispectral image (MSI), hyperspectral image (HSI), light detection and ranging (LiDAR). | ||||||||||||||||||
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: | 20 Jun 2017 15:50 | ||||||||||||||||||
Last Modified: | 31 Jul 2019 20:10 |
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