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Fusing Multi-seasonal Sentinel-2 Imagery for Urban Land Cover Classification with Multibranch Residual Convolutional Neural Networks

Qiu, Chunping and Mou, LiChao and Schmitt, Michael and Zhu, Xiao Xiang (2020) Fusing Multi-seasonal Sentinel-2 Imagery for Urban Land Cover Classification with Multibranch Residual Convolutional Neural Networks. IEEE Geoscience and Remote Sensing Letters. IEEE - Institute of Electrical and Electronics Engineers. ISSN 1545-598X (In Press)

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Abstract

Exploiting multitemporal Sentinel-2 images for urban land cover classification has become an important research topic, since these images have become globally available at relatively fine temporal resolution, thus offering great potential for large-scale land cover mapping. However, appropriate exploitation of the images needs to address problems such as cloud cover inherent to optical satellite imagery. To this end, we propose a simple yet effective decision-level fusion approach for urban land cover prediction from multiseasonal Sentinel-2 images, using the state-of-the-art residual convolutional neural networks (ResNet). We extensively tested the approach in a cross-validation manner over a seven-city study area in central Europe. Both quantitative and qualitative results demonstrated the superior performance of the proposed fusion approach over several baseline approaches, including observation- and feature-level fusion.

Item URL in elib:https://elib.dlr.de/134124/
Document Type:Article
Title:Fusing Multi-seasonal Sentinel-2 Imagery for Urban Land Cover Classification with Multibranch Residual Convolutional Neural Networks
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Qiu, ChunpingTUMUNSPECIFIED
Mou, LiChaoLiChao.Mou (at) dlr.deUNSPECIFIED
Schmitt, MichaelMichael.Schmitt (at) tum.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:2020
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
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1545-598X
Status:In Press
Keywords:Classification, fusion, long short-term memory (LSTM), multitemporal, nonlocal, residual convolutional neural network (ResNet), Sentinel-2, urban land cover.
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: Mou, LiChao
Deposited On:18 Feb 2020 14:27
Last Modified:19 Feb 2020 09:50

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