Davydova, Ksenia and Cui, Shiyong and Reinartz, Peter (2016) Building footprint extraction from Digital Surface Models using Neural Networks. In: Proceedings of SPIE, 10004, pp. 1-10. SPIE Remote Sensing 2016, 26.-29. Sep 2016, Edinburgh. doi: 10.1117/12.2240727.
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Official URL: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2571485
Abstract
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.
Item URL in elib: | https://elib.dlr.de/108368/ | ||||||||||||||||
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Document Type: | Conference or Workshop Item (Speech) | ||||||||||||||||
Title: | Building footprint extraction from Digital Surface Models using Neural Networks | ||||||||||||||||
Authors: |
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Date: | 2016 | ||||||||||||||||
Journal or Publication Title: | Proceedings of SPIE | ||||||||||||||||
Refereed publication: | Yes | ||||||||||||||||
Open Access: | Yes | ||||||||||||||||
Gold Open Access: | No | ||||||||||||||||
In SCOPUS: | No | ||||||||||||||||
In ISI Web of Science: | Yes | ||||||||||||||||
Volume: | 10004 | ||||||||||||||||
DOI: | 10.1117/12.2240727 | ||||||||||||||||
Page Range: | pp. 1-10 | ||||||||||||||||
Editors: |
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Status: | Published | ||||||||||||||||
Keywords: | Building footprint extraction, binary mask, Digital Surface Model, neural networks, Markov Random Fields, Normalized Difference Vegetation Index | ||||||||||||||||
Event Title: | SPIE Remote Sensing 2016 | ||||||||||||||||
Event Location: | Edinburgh | ||||||||||||||||
Event Type: | international Conference | ||||||||||||||||
Event Dates: | 26.-29. Sep 2016 | ||||||||||||||||
Organizer: | SPIE Remote Sensing | ||||||||||||||||
HGF - Research field: | Aeronautics, Space and Transport | ||||||||||||||||
HGF - Program: | Space | ||||||||||||||||
HGF - Program Themes: | Earth Observation | ||||||||||||||||
DLR - Research area: | Raumfahrt | ||||||||||||||||
DLR - Program: | R EO - Earth Observation | ||||||||||||||||
DLR - Research theme (Project): | R - Vorhaben hochauflösende Fernerkundungsverfahren (old) | ||||||||||||||||
Location: | Oberpfaffenhofen | ||||||||||||||||
Institutes and Institutions: | Remote Sensing Technology Institute > Photogrammetry and Image Analysis | ||||||||||||||||
Deposited By: | Bittner, Ksenia | ||||||||||||||||
Deposited On: | 25 Nov 2016 13:21 | ||||||||||||||||
Last Modified: | 29 Mar 2023 00:30 |
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