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Deep Fusion of Remote Sensing Data for Accurate Classification

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/
Document Type:Article
Title:Deep Fusion of Remote Sensing Data for Accurate Classification
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Chen, Yushiharbin institute of technologyUNSPECIFIED
Li, Chunyangharbin institute of technologyUNSPECIFIED
Ghamisi, Pedramdlr-imf/tum-lmfUNSPECIFIED
Jia, Xiupinguniversity of new south walesUNSPECIFIED
Gu, Yanfengharbin institute of technologyUNSPECIFIED
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:
EditorsEmail
Frery, Alejandro C.acfrery@gmail.com
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 - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
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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