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Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset

Qiu, Chunping und Tong, Xiaochong und Schmitt, Michael und Bechtel, Benjamin und Zhu, Xiao Xiang (2020) Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, Seiten 2793-2806. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2020.2995711. ISSN 1939-1404.

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Offizielle URL: https://ieeexplore.ieee.org/document/9103196

Kurzfassung

As a unique classification scheme for urban forms and functions, the local climate zone (LCZ) system provides essential general information for any studies related to urban environments, especially on a large scale. Remote sensing data-based classification approaches are the key to large-scale mapping and monitoring of LCZs. The potential of deep learning-based approaches is not yet fully explored, even though advanced convolutional neural networks (CNNs) continue to push the frontiers for various computer vision tasks. One reason is that published studies are based on different datasets, usually at a regional scale, which makes it impossible to fairly and consistently compare the potential of different CNNs for real-world scenarios. This article is based on the big So2Sat LCZ42 benchmark dataset dedicated to LCZ classification. Using this dataset, we studied a range of CNNs of varying sizes. In addition, we proposed a CNN to classify LCZs from Sentinel-2 images, Sen2LCZ-Net. Using this base network, we propose fusing multilevel features using the extended Sen2LCZ-Net-MF. With this proposed simple network architecture, and the highly competitive benchmark dataset, we obtain results that are better than those obtained by the state-of-the-art CNNs, while requiring less computation with fewer layers and parameters. Large-scale LCZ classification examples of completely unseen areas are presented, demonstrating the potential of our proposed Sen2LCZ-Net-MF as well as the So2Sat LCZ42 dataset. We also intensively investigated the influence of network depth and width, and the effectiveness of the design choices made for Sen2LCZ-Net-MF. This article will provide important baselines for future CNN-based algorithm developments for both LCZ classification and other urban land cover land use classification. Code and pretrained models are available at https://github.com/ChunpingQiu/benchmark-on-So2SatLCZ42-dataset-a-simple-tour.

elib-URL des Eintrags:https://elib.dlr.de/138018/
Dokumentart:Zeitschriftenbeitrag
Titel:Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Qiu, ChunpingTechnichal University MünchenNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Tong, XiaochongInformation Engineering University, ZhengzhouNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Schmitt, MichaelTUMNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Bechtel, BenjaminInstitute of Geography, Ruhr-University Bochum, GermanyNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Zhu, Xiao Xiangxiao.zhu (at) dlr.dehttps://orcid.org/0000-0001-5530-3613NICHT SPEZIFIZIERT
Datum:2020
Erschienen in:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Ja
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:13
DOI:10.1109/JSTARS.2020.2995711
Seitenbereich:Seiten 2793-2806
Verlag:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1939-1404
Status:veröffentlicht
Stichwörter:Benchmark, convolutional neural networks, local climate zones, Sentinel-2, urban land cover
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Vorhaben hochauflösende Fernerkundungsverfahren (alt), R - Optische Fernerkundung
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > EO Data Science
Hinterlegt von: Liu, Rong
Hinterlegt am:26 Nov 2020 11:54
Letzte Änderung:24 Okt 2023 12:02

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