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Automatic Building Footprint Extraction from Multi-Resolution Remote Sensing Images Using a Hybrid FCN

Schuegraf, Philipp und Bittner, Ksenia (2019) Automatic Building Footprint Extraction from Multi-Resolution Remote Sensing Images Using a Hybrid FCN. ISPRS International Journal of Geo-Information, 8 (4), Seiten 191-207. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/ijgi8040191. ISSN 2220-9964.

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Offizielle URL: https://www.mdpi.com/2220-9964/8/4/191

Kurzfassung

Recent technical developments made it possible to supply large-scale satellite image coverage. This poses the challenge of efficient discovery of imagery. One very important task in applications like urban planning and reconstruction is to automatically extract building footprints. The integration of different information, which is presently achievable due to the availability of high-resolution remote sensing data sources, makes it possible to improve the quality of the extracted building outlines. Recently, deep neural networks were extended from image-level to pixel-level labelling, allowing to densely predict semantic labels. Based on these advances, we propose an end-to-end U-shaped neural network, which efficiently merges depth and spectral information within two parallel networks combined at the late stage for binary building mask generation. Moreover, as satellites usually provide high-resolution panchromatic images, but only low-resolution multi-spectral images, we tackle this issue by using a residual neural network block. It fuses those images with different spatial resolution at the early stage, before passing the fused information to the Unet stream, responsible for processing spectral information. In a parallel stream, a stereo digital surface model (DSM) is also processed by the Unet. Additionally, we demonstrate that our method generalizes for use in cities which are not included in the training data.

elib-URL des Eintrags:https://elib.dlr.de/127862/
Dokumentart:Zeitschriftenbeitrag
Titel:Automatic Building Footprint Extraction from Multi-Resolution Remote Sensing Images Using a Hybrid FCN
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Schuegraf, Philippschuegra (at) hm.eduNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Bittner, KseniaKsenia.Bittner (at) dlr.dehttps://orcid.org/0000-0002-4048-3583NICHT SPEZIFIZIERT
Datum:12 April 2019
Erschienen in:ISPRS International Journal of Geo-Information
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Ja
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:8
DOI:10.3390/ijgi8040191
Seitenbereich:Seiten 191-207
Verlag:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2220-9964
Status:veröffentlicht
Stichwörter:deep learning; building footprint extraction; fully convolutional neural network; World View-2 Imagery; Unet; stereo imagery; stereo DSM; pansharpening
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Verkehr
HGF - Programmthema:Straßenverkehr
DLR - Schwerpunkt:Verkehr
DLR - Forschungsgebiet:V ST Straßenverkehr
DLR - Teilgebiet (Projekt, Vorhaben):V - D.MoVe (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > Photogrammetrie und Bildanalyse
Hinterlegt von: Bittner, Ksenia
Hinterlegt am:14 Jun 2019 10:31
Letzte Änderung:31 Okt 2023 14:56

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