Molch, Katrin and Gamba, P. and Kayitakire, F. (2010) Performance of built-up area classifications using high-resolution SAR data. Canadian Journal of Remote Sensing, 36 (3), pp. 197-210. ISSN 1712-7971.
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Identification of the built-up area from satellite imagery can provide a crucial information layer in disaster mitigation and management and for monitoring urban sprawl e.g. in developing countries. Spaceborne radar imagery is at an advantage in regions where environmental conditions impede the acquisition of optical image data. Automated exploitation procedures are imperative for efficient, large area coverage. However, methodologies must be developed or adapted to account for the specific characteristics of synthetic aperture radar (SAR) data. This study evaluates the identification of the built-up area on RADARSAT-1 Fine Mode and ENVISAT Image Mode data using the texture-based, anisotropic, rotation-invariant built-up presence index. Data selection and processing parameters are discussed. User’s accuracies of up to 77.5%, with overall accuracies of up to 81.3%, were achieved in this comparative study without any post-classification editing.
|Title:||Performance of built-up area classifications using high-resolution SAR data|
|Journal or Publication Title:||Canadian Journal of Remote Sensing|
|In Open Access:||No|
|In ISI Web of Science:||No|
|Page Range:||pp. 197-210|
|Keywords:||Urban footprint, SAR, texture, classification accuracy, built-up area|
|HGF - Research field:||Aeronautics, Space and Transport (old)|
|HGF - Program:||Space (old)|
|HGF - Program Themes:||W EO - Erdbeobachtung|
|DLR - Research area:||Space|
|DLR - Program:||W EO - Erdbeobachtung|
|DLR - Research theme (Project):||W - Vorhaben Geowissenschaftl. Fernerkundungs- und GIS-Verfahren (old)|
|Institutes and Institutions:||German Remote Sensing Data Center|
|Deposited By:||Molch, Katrin|
|Deposited On:||23 Nov 2010 13:57|
|Last Modified:||26 May 2016 05:03|
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