Shi, Yilei und Li, Qingyu und Zhu, Xiao Xiang (2019) Building Footprint Generation using Improved Generative Adversarial Networks. IEEE Geoscience and Remote Sensing Letters, 16 (4), Seiten 603-607. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2018.2878486. ISSN 1545-598X.
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Offizielle URL: https://ieeexplore.ieee.org/document/8581486
Kurzfassung
Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes. In this letter, we have proposed improved generative adversarial networks (GANs) for the automatic generation of building footprints from satellite images. We used a conditional GAN (CGAN) with a cost function derived from the Wasserstein distance and added a gradient penalty term. The achieved results indicated that the proposed method can significantly improve the quality of building footprint generation compared to CGANs, the U-Net, and other networks. In addition, our method nearly removes all hyperparameters tuning.
elib-URL des Eintrags: | https://elib.dlr.de/122453/ | ||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||
Titel: | Building Footprint Generation using Improved Generative Adversarial Networks | ||||||||||||||||
Autoren: |
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Datum: | 2019 | ||||||||||||||||
Erschienen in: | IEEE Geoscience and Remote Sensing Letters | ||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||
Open Access: | Ja | ||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||
Band: | 16 | ||||||||||||||||
DOI: | 10.1109/LGRS.2018.2878486 | ||||||||||||||||
Seitenbereich: | Seiten 603-607 | ||||||||||||||||
Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||
ISSN: | 1545-598X | ||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||
Stichwörter: | Building footprint, conditional generative adversarial networks (CGANs), generative adversarial networks (GANs), segmentation, Wasserstein GANs (WGANs) | ||||||||||||||||
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 - Fernerkundung u. Geoforschung | ||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||
Hinterlegt von: | Hoffmann, Eike Jens | ||||||||||||||||
Hinterlegt am: | 23 Okt 2018 14:50 | ||||||||||||||||
Letzte Änderung: | 08 Nov 2023 10:36 |
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