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Deep learning decision fusion for the classification of urban remote sensing data

Abdi, Ghasem and Samadzadegan, Farhad and Reinartz, Peter (2018) Deep learning decision fusion for the classification of urban remote sensing data. Journal of Applied Remote Sensing, 12 (1), pp. 1-19. Society of Photo-optical Instrumentation Engineers (SPIE). DOI: 10.1117/1.JRS.12.016038 ISSN 1931-3195

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Official URL: https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-12/issue-01/016038/Deep-learning-decision-fusion-for-the-classification-of-urban-remote/10.1117/1.JRS.12.016038.full

Abstract

Multisensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extraction has recently attracted significant interest and has become a hot research topic in the geoscience and remote sensing research community. A deep learning decision fusion approach is presented to perform multisensor urban remote sensing data classification. After deep features are extracted by utilizing joint spectral–spatial information, a soft-decision made classifier is applied to train high-level feature representations and to fine-tune the deep learning framework. Next, a decision-level fusion classifies objects of interest by the joint use of sensors. Finally, a context-aware object-based postprocessing is used to enhance the classification results. A series of comparative experiments are conducted on the widely used dataset of 2014 IEEE GRSS data fusion contest. The obtained results illustrate the considerable advantages of the proposed deep learning decision fusion over the traditional classifiers.

Item URL in elib:https://elib.dlr.de/119394/
Document Type:Article
Title:Deep learning decision fusion for the classification of urban remote sensing data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Abdi, Ghasemghasem.abdi (at) ut.ac.irUNSPECIFIED
Samadzadegan, Farhadfarhad.samadzadegan (at) dlr.deUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475
Date:13 March 2018
Journal or Publication Title:Journal of Applied Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:12
DOI :10.1117/1.JRS.12.016038
Page Range:pp. 1-19
Publisher:Society of Photo-optical Instrumentation Engineers (SPIE)
ISSN:1931-3195
Status:Published
Keywords:deep learning, multisensor data Fusion, classification
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Traffic Management (old)
DLR - Research area:Transport
DLR - Program:V VM - Verkehrsmanagement
DLR - Research theme (Project):V - Vabene++ (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Zielske, Mandy
Deposited On:22 Mar 2018 20:23
Last Modified:31 Jul 2019 20:16

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