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, 17 (10), pp. 1787-1791. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2019.2953497. ISSN 1545-598X.
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Official URL: https://ieeexplore.ieee.org/document/8951229
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/ | ||||||||||||||||||||
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Document Type: | Article | ||||||||||||||||||||
Title: | Fusing Multi-seasonal Sentinel-2 Imagery for Urban Land Cover Classification with Multibranch Residual Convolutional Neural Networks | ||||||||||||||||||||
Authors: |
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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 | ||||||||||||||||||||
Volume: | 17 | ||||||||||||||||||||
DOI: | 10.1109/LGRS.2019.2953497 | ||||||||||||||||||||
Page Range: | pp. 1787-1791 | ||||||||||||||||||||
Publisher: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||||||
ISSN: | 1545-598X | ||||||||||||||||||||
Status: | Published | ||||||||||||||||||||
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 - Earth Observation | ||||||||||||||||||||
DLR - Research theme (Project): | R - Vorhaben hochauflösende Fernerkundungsverfahren (old) | ||||||||||||||||||||
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: | 24 Oct 2023 12:45 |
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