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Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network

Qiu, Chunping und Mou, LiChao und Schmitt, Michael und Zhu, Xiao Xiang (2019) Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network. ISPRS Journal of Photogrammetry and Remote Sensing, 154, Seiten 151-162. Elsevier. doi: 10.1016/j.isprsjprs.2019.05.004. ISSN 0924-2716.

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Kurzfassung

The local climate zone (LCZ) scheme was originally proposed to provide an interdisciplinary taxonomy for urban heat island (UHI) studies. In recent years, the scheme has also become a starting point for the development of higher-level products, as the LCZ classes can help provide a generalized understanding of urban structures and land uses. LCZ mapping can therefore theoretically aid in fostering a better understanding of spatio-temporal dynamics of cities on a global scale. However, reliable LCZ maps are not yet available globally. As a first step toward automatic LCZ mapping, this work focuses on LCZ-derived land cover classification, using multi-seasonal Sentinel-2 images. We propose a recurrent residual network (Re-ResNet) architecture that is capable of learning a joint spectral-spatial-temporal feature representation within a unitized framework. To this end, a residual convolutional neural network (ResNet) and a recurrent neural network (RNN) are combined into one end-to-end architecture. The ResNet is able to learn rich spectral-spatial feature representations from single-seasonal imagery, while the RNN can effectively analyze temporal dependencies of multi-seasonal imagery. Cross validations were carried out on a diverse dataset covering seven distinct European cities, and a quantitative analysis of the experimental results revealed that the combined use of the multi-temporal information and Re-ResNet results in an improvement of approximately 7 percent points in overall accuracy. The proposed framework has the potential to produce consistent-quality urban land cover and LCZ maps on a large scale, to support scientific progress in fields such as urban geography and urban climatology.

elib-URL des Eintrags:https://elib.dlr.de/128132/
Dokumentart:Zeitschriftenbeitrag
Titel:Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Qiu, Chunpingtu münchenNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Mou, LiChaoLiChao.Mou (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Schmitt, Michaelm.schmitt (at) tum.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Zhu, Xiao Xiangxiao.zhu (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:2019
Erschienen in:ISPRS Journal of Photogrammetry and Remote Sensing
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:154
DOI:10.1016/j.isprsjprs.2019.05.004
Seitenbereich:Seiten 151-162
Verlag:Elsevier
ISSN:0924-2716
Status:veröffentlicht
Stichwörter:Land cover Local climate zones (LCZs) Sentinel-2 Multi-seasonal Residual convolutional neural network (ResNet) Long short-term memory (LSTM) Recurrent neural network (RNN)
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)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > EO Data Science
Hinterlegt von: Hoffmann, Eike Jens
Hinterlegt am:29 Okt 2019 12:32
Letzte Änderung:03 Nov 2023 09:18

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