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Slum mapping in polarimetric SAR data using spatial features

Wurm, Michael and Taubenböck, Hannes and Weigand, Matthias and Schmitt, Andreas (2017) Slum mapping in polarimetric SAR data using spatial features. Remote Sensing of Environment, 194, pp. 190-204. Elsevier. DOI: 10.1016/j.rse.2017.03.030 ISBN ISSN 0034-4257 ISSN 0034-4257

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Official URL: http://www.sciencedirect.com/science/article/pii/S0034425717301335

Abstract

Driven by massive urbanization processes, particularly in developing countries, people flock into the cities resulting in an evolution of large slum areas. Mapping and monitoring of slum areas have become an invaluable source for decision-making processes to implement policies related to improve living conditions. Space-borne remotely sensed data has been explored in the past for slum mapping, however, to a large extent supported by optical imagery. In this paper, we explore the capabilities of dual-polarized (HH/VV and VV/VH) X-band Synthetic Aperture Radar (SAR) from TerraSAR-X images for slum extent mapping using the Kennaugh element framework for image preprocessing. In this way, spatial image descriptors based on texture, morphological profiles and polarimetric features have been tested at various window sizes [11 × 11, ... 161 × 161] for mapping slums using the random forest classifier in a series of experiments. For benchmarking the classification results, LDA as parametric linear classifier is used for comparison. Classification performance was evaluated by comparison with a reference map indicating that texture features hold the highest contribution to discriminating slums from other urban structures. Best window size was found using a spatial neighborhood of 81 × 81 pixels resulting in Overall Accuracy of 88.58 and Kappa of 0.7809 for RF classifier. A patch-based analysis of classification results reveals areal dependencies of the classifier in terms of larger slum patches that are mapped with higher precision than smaller patches. Analyses including additional spatial image descriptors based on mathematical profiles reveal no significant contribution to the classification result.

Item URL in elib:https://elib.dlr.de/111787/
Document Type:Article
Title:Slum mapping in polarimetric SAR data using spatial features
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Wurm, Michaelmichael.wurm (at) dlr.dehttps://orcid.org/0000-0001-5967-1894
Taubenböck, Hanneshannes.taubenboeck (at) dlr.dehttps://orcid.org/0000-0003-4360-9126
Weigand, Matthiasmatthias.weigand (at) dlr.dehttps://orcid.org/0000-0002-5553-4152
Schmitt, Andreasandreas.schmitt (at) dlr.deUNSPECIFIED
Date:June 2017
Journal or Publication Title:Remote Sensing of Environment
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:194
DOI :10.1016/j.rse.2017.03.030
Page Range:pp. 190-204
Publisher:Elsevier
ISSN:0034-4257
ISBN:ISSN 0034-4257
Status:Published
Keywords:Slums; Informal settlements; polSAR; Classification; Random forests; Kennaugh elements; Texture; Morphological profiles
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Zivile Kriseninformation und Georisiken (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Wurm, Michael
Deposited On:04 Jul 2017 11:03
Last Modified:04 Jul 2017 11:03

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