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Building Footprint Extraction From VHR Remote Sensing Images Combined With Normalized DSMs Using Fused Fully Convolutional Networks

Bittner, Ksenia and Adam, Fathalrahman and Cui, Shiyong and Körner, Marco and Reinartz, Peter (2018) Building Footprint Extraction From VHR Remote Sensing Images Combined With Normalized DSMs Using Fused Fully Convolutional Networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11 (8), pp. 2615-2629. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/JSTARS.2018.2849363 ISSN 1939-1404

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Official URL: https://ieeexplore.ieee.org/document/8447548?denied=

Abstract

Automatic building extraction and delineation from high-resolution satellite imagery is an important but very challenging task, due to the extremely large diversity of building appearances. Nowadays, it is possible to use multiple high-resolution remote sensing data sources, which allow the integration of different information in order to improve the extraction accuracy of building outlines. Many algorithms are built on spectral-based or appearance-based criteria, from single or fused data sources, to perform the building footprint extraction. But the features for these algorithms are usually manually extracted, which limits their accuracy. Recently developed fully convolutional networks (FCNs), which are similar to normal convolutional neural networks (CNN), but the last fully connected layer is replaced by another convolution layer with a large "receptive field", quickly became the state-of-theart method for image recognition tasks, as they bring the possibility to perform dense pixelwise classification of input images. Based on these advantages, i.e., the automatic extraction of relevant features, and dense classification of images, we propose an end-to-end FCN, which effectively combines the spectral and height information from different data sources and automatically generates a full resolution binary building mask. Our architecture (FUSED-FCN4S) consists of three parallel networks merged at a late stage, which helps propagating fine detailed information from earlier layers to higher levels, in order to produce an output with more accurate building outlines. The inputs to the proposed Fused-FCN4s are three-band (RGB), panchromatic (PAN), and normalized digital surface model (nDSM) images. Experimental results demonstrate that the fusion of several networks is able to achieve excellent results on complex data. Moreover, the developed model was successfully applied to different cities to show its generalization capacity.

Item URL in elib:https://elib.dlr.de/122645/
Document Type:Article
Title:Building Footprint Extraction From VHR Remote Sensing Images Combined With Normalized DSMs Using Fused Fully Convolutional Networks
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Bittner, Kseniaksenia.bittner (at) dlr.dehttps://orcid.org/0000-0002-4048-3583
Adam, FathalrahmanTUMUNSPECIFIED
Cui, ShiyongRemote Sensing Technology Institute (IMF)https://orcid.org/0000-0002-5417-4482
Körner, Marcomarco.koerner (at) tum.deUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475
Date:August 2018
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:11
DOI :10.1109/JSTARS.2018.2849363
Page Range:pp. 2615-2629
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1939-1404
Status:Published
Keywords:Binary classification, building footprint, data fusion, deep learning, fully convolutional networks (FCNs), satellite images
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Traffic Management (old)
DLR - Research area:Transport
DLR - Program:V VM - Verkehrsmanagement
DLR - Research theme (Project):V - Vabene++ (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Zielske, Mandy
Deposited On:20 Nov 2018 10:46
Last Modified:31 Jul 2019 20:20

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