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

Schuegraf, Philipp and 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), pp. 191-207. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/ijgi8040191 ISSN 2220-9964

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

Abstract

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.

Item URL in elib:https://elib.dlr.de/127862/
Document Type:Article
Title:Automatic Building Footprint Extraction from Multi-Resolution Remote Sensing Images Using a Hybrid FCN
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Schuegraf, Philippschuegra (at) hm.eduUNSPECIFIED
Bittner, KseniaKsenia.Bittner (at) dlr.dehttps://orcid.org/0000-0002-4048-3583
Date:12 April 2019
Journal or Publication Title:ISPRS International Journal of Geo-Information
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:No
Volume:8
DOI :10.3390/ijgi8040191
Page Range:pp. 191-207
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2220-9964
Status:Published
Keywords:deep learning; building footprint extraction; fully convolutional neural network; World View-2 Imagery; Unet; stereo imagery; stereo DSM; pansharpening
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - D.MoVe
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Bittner, Ksenia
Deposited On:14 Jun 2019 10:31
Last Modified:21 Sep 2019 05:05

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