Davydova, Ksenia und Cui, Shiyong und Reinartz, Peter (2016) Building footprint extraction from Digital Surface Models using Neural Networks. In: Proceedings of SPIE, 10004, Seiten 1-10. SPIE Remote Sensing 2016, 2016-09-26 - 2016-09-29, Edinburgh. doi: 10.1117/12.2240727.
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Offizielle URL: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2571485
Kurzfassung
Two-dimensional building footprints are a basis for many applications: from cartography to three-dimensional building models generation. Although, many methodologies have been proposed for building footprint extraction, this topic remains an open research area. Neural networks are able to model the complex relationships between the multivariate input vector and the target vector. Based on these abilities we propose a methodology using neural networks and Markov Random Fields (MRF) for automatic building footprint extraction from normalized Digital Surface Model (nDSM) and satellite images within urban areas. The proposed approach has mainly two steps. In the first step, the unary terms are learned for the MRF energy function by a four-layer neural network. The neural network is learned on a large set of patches consisting of both nDSM and Normalized Difference Vegetation Index (NDVI). Then prediction is performed to calculate the unary terms that are used in the MRF. In the second step, the energy function is minimized using a max ow algorithm, which leads to a binary building mask. The building extraction results are compared with available ground truth. The comparison illustrates the efficiency of the proposed algorithm which can extract approximately 80% of buildings from nDSM with high accuracy.
elib-URL des Eintrags: | https://elib.dlr.de/108368/ | ||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||
Titel: | Building footprint extraction from Digital Surface Models using Neural Networks | ||||||||||||||||
Autoren: |
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Datum: | 2016 | ||||||||||||||||
Erschienen in: | Proceedings of SPIE | ||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||
Open Access: | Ja | ||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||
In SCOPUS: | Nein | ||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||
Band: | 10004 | ||||||||||||||||
DOI: | 10.1117/12.2240727 | ||||||||||||||||
Seitenbereich: | Seiten 1-10 | ||||||||||||||||
Herausgeber: |
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Status: | veröffentlicht | ||||||||||||||||
Stichwörter: | Building footprint extraction, binary mask, Digital Surface Model, neural networks, Markov Random Fields, Normalized Difference Vegetation Index | ||||||||||||||||
Veranstaltungstitel: | SPIE Remote Sensing 2016 | ||||||||||||||||
Veranstaltungsort: | Edinburgh | ||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||
Veranstaltungsbeginn: | 26 September 2016 | ||||||||||||||||
Veranstaltungsende: | 29 September 2016 | ||||||||||||||||
Veranstalter : | SPIE Remote Sensing | ||||||||||||||||
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 > Photogrammetrie und Bildanalyse | ||||||||||||||||
Hinterlegt von: | Bittner, Ksenia | ||||||||||||||||
Hinterlegt am: | 25 Nov 2016 13:21 | ||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:13 |
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