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Urban Area Extent Extraction in Spaceborne HR and VHR Data Using Multi-Resolution Features

Department of Industrial and Information Engineering, University of Pavia, Via Ferrata 5, Pavia 27100, Italy
Institute for Advanced Studies, Palazzo della Vittoria, Pavia 27100, Italy
Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Marquês de São Vicente, 225, Gávea 22451-900, Brazil
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2014, 14(10), 18337-18352;
Received: 16 July 2014 / Revised: 11 September 2014 / Accepted: 15 September 2014 / Published: 30 September 2014
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
PDF [1380 KB, uploaded 30 September 2014]


Detection of urban area extents by means of remotely sensed data is a difficult task, especially because of the multiple, diverse definitions of what an “urban area” is. The models of urban areas listed in technical literature are based on the combination of spectral information with spatial patterns, possibly at different spatial resolutions. Starting from the same data set, “urban area” extraction may thus lead to multiple outputs. If this is done in a well-structured framework, however, this may be considered as an advantage rather than an issue. This paper proposes a novel framework for urban area extent extraction from multispectral Earth Observation (EO) data. The key is to compute and combine spectral and multi-scale spatial features. By selecting the most adequate features, and combining them with proper logical rules, the approach allows matching multiple urban area models. Experimental results for different locations in Brazil and Kenya using High-Resolution (HR) data prove the usefulness and flexibility of the framework. View Full-Text
Keywords: urban areas; multi-resolution processing; feature fusion urban areas; multi-resolution processing; feature fusion
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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Iannelli, G.C.; Lisini, G.; Dell'Acqua, F.; Feitosa, R.Q.; Costa, G.A.O.P.; Gamba, P. Urban Area Extent Extraction in Spaceborne HR and VHR Data Using Multi-Resolution Features. Sensors 2014, 14, 18337-18352.

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