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Fusing Multi-Seasonal Sentinel-2 Images with Residual Convolutional Neural Networks for Local Climate Zone-Derived Urban Land Cover Classification

Qiu, Chunping and Schmitt, Michael and Zhu, Xiao Xiang (2019) Fusing Multi-Seasonal Sentinel-2 Images with Residual Convolutional Neural Networks for Local Climate Zone-Derived Urban Land Cover Classification. IGARSS 2019, 28.7.-2.8.19, Yokohama/Japan. DOI: 10.1109/IGARSS.2019.8898223

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Official URL: https://ieeexplore.ieee.org/document/8898223

Abstract

This paper proposes a framework to fuse multi-seasonal Sentinel-2 images, with application on LCZ-derived urban land cover classification. Cross-validation over a seven-city study area in central Europe demonstrates its consistently better performance over several previous approaches, with the same experimental setup. Based on our previous work, we can conclude that decision-level fusion is better than feature-level fusion for similar tasks at similar scale with multi-seasonal Sentinel-2 images. With the framework, urban land cover maps of several cities are produced. The visualization of two exemplary areas shows urban structures that are consistent with existing datasets. This framework can be also generally beneficial for other types of urban mapping.

Item URL in elib:https://elib.dlr.de/132447/
Document Type:Conference or Workshop Item (Speech)
Title:Fusing Multi-Seasonal Sentinel-2 Images with Residual Convolutional Neural Networks for Local Climate Zone-Derived Urban Land Cover Classification
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Qiu, ChunpingTUMUNSPECIFIED
Schmitt, MichaelMichael.Schmitt (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:2019
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.1109/IGARSS.2019.8898223
Page Range:pp. 5037-5040
Status:Published
Keywords:Sentinel-2, Classification, residual convolutional neural network (ResNet), urban land cover, long short-term memory (LSTM)
Event Title:IGARSS 2019
Event Location:Yokohama/Japan
Event Type:international Conference
Event Dates:28.7.-2.8.19
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: Hong, Danfeng
Deposited On:06 Dec 2019 16:40
Last Modified:06 Dec 2019 16:40

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