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

1,† and 2,*,†
1
Department for Computer Science and Mathematics, University of Applied Sciences Munich (HM), Loth Str. 64, 80335 München, Germany
2
German Aerospace Center (DLR), Remote Sensing Technology Institute, Münchner Str. 20, 82234 Weßling, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
ISPRS Int. J. Geo-Inf. 2019, 8(4), 191; https://doi.org/10.3390/ijgi8040191
Received: 26 February 2019 / Revised: 29 March 2019 / Accepted: 6 April 2019 / Published: 12 April 2019
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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. View Full-Text
Keywords: deep learning; building footprint extraction; fully convolutional neural network; World View-2 Imagery; Unet; stereo imagery; stereo DSM; pansharpening deep learning; building footprint extraction; fully convolutional neural network; World View-2 Imagery; Unet; stereo imagery; stereo DSM; pansharpening
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Schuegraf, P.; Bittner, K. Automatic Building Footprint Extraction from Multi-Resolution Remote Sensing Images Using a Hybrid FCN. ISPRS Int. J. Geo-Inf. 2019, 8, 191.

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