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Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones

Zhang, Guichen and Ghamisi, Pedram and Zhu, Xiao Xiang (2019) Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones. IEEE Transactions on Geoscience and Remote Sensing, 57 (10), pp. 7623-7642. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/TGRS.2019.2914967 ISSN 0196-2892

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Official URL: https://ieeexplore.ieee.org/document/8765334

Abstract

This paper proposes a novel framework for fusing multi-temporal, multispectral satellite images and OpenStreetMap(OSM) data for the classification of local climate zones (LCZs).Feature stacking is the most commonly-used method of data fusion but does not consider the heterogeneity of multimodal optical images and OSM data, which becomes its main drawback. The proposed framework processes two data sources separately and then combines them at the model level through two fusion models (the land use fusion model and building fusion model),which aim to fuse optical images with land use and buildings layers of OSM data, respectively. In addition, a new approach to detecting building incompleteness of OSM data is proposed. The proposed framework was trained and tested using data from the2017 IEEE GRSS Data Fusion Contest, and further validated on one additional test set containing test samples which are manually labeled in Munich and New York. Experimental results have indicated that compared to the feature stacking-based baseline framework the proposed framework is effective in fusing optical images with OSM data for the classification of LCZs with high generalization capability on a large scale. The classification accuracy of the proposed framework outperforms the baseline framework by more than 6% and 2%, while testing on the test set of 2017 IEEE GRSS Data Fusion Contest and the additional test set, respectively. In addition, the proposed framework is less sensitive to spectral diversities of optical satellite images and thus achieves more stable classification performance than state-of-the-art frameworks.

Item URL in elib:https://elib.dlr.de/128185/
Document Type:Article
Additional Information:so2sat
Title:Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Zhang, GuichenGuichen.Zhang (at) dlr.deUNSPECIFIED
Ghamisi, PedramHelmholtz-Zentrum Dresden-RossendorfUNSPECIFIED
Zhu, Xiao Xiangxiaoxiang.zhu (at) dlr.dehttps://orcid.org/0000-0001-5530-3613
Date:July 2019
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:57
DOI :10.1109/TGRS.2019.2914967
Page Range:pp. 7623-7642
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:Local climate zones (LCZs), heterogeneous datafusion, satellite images, OpenStreetMap (OSM), canonical corre-lation forest (CCF)
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 > EO Data Science
Deposited By: Wang, Yuanyuan
Deposited On:03 Jul 2019 09:55
Last Modified:07 Jan 2020 11:16

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