Urban Area Extent Extraction in Spaceborne HR and VHR Data Using Multi-Resolution Features
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 pattern...
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-09-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/14/10/18337 |
| _version_ | 1828120666594869248 |
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| author | Gianni Cristian Iannelli Gianni Lisini Fabio Dell'Acqua Raul Queiroz Feitosa Gilson Alexandre Ostwald Pedro da Costa Paolo Gamba |
| author_facet | Gianni Cristian Iannelli Gianni Lisini Fabio Dell'Acqua Raul Queiroz Feitosa Gilson Alexandre Ostwald Pedro da Costa Paolo Gamba |
| author_sort | Gianni Cristian Iannelli |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T14:08:42Z |
| format | Article |
| id | doaj.art-fd2b62cb91814762b2577e8f7631019c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T14:08:42Z |
| publishDate | 2014-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fd2b62cb91814762b2577e8f7631019c2022-12-22T04:19:48ZengMDPI AGSensors1424-82202014-09-011410183371835210.3390/s141018337s141018337Urban Area Extent Extraction in Spaceborne HR and VHR Data Using Multi-Resolution FeaturesGianni Cristian Iannelli0Gianni Lisini1Fabio Dell'Acqua2Raul Queiroz Feitosa3Gilson Alexandre Ostwald Pedro da Costa4Paolo Gamba5Department of Industrial and Information Engineering, University of Pavia, Via Ferrata 5, Pavia 27100, ItalyInstitute for Advanced Studies, Palazzo della Vittoria, Pavia 27100, ItalyDepartment of Industrial and Information Engineering, University of Pavia, Via Ferrata 5, Pavia 27100, ItalyDepartment of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Marquês de São Vicente, 225, Gávea 22451-900, BrazilDepartment of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Marquês de São Vicente, 225, Gávea 22451-900, BrazilDepartment of Industrial and Information Engineering, University of Pavia, Via Ferrata 5, Pavia 27100, ItalyDetection 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.http://www.mdpi.com/1424-8220/14/10/18337urban areasmulti-resolution processingfeature fusion |
| spellingShingle | Gianni Cristian Iannelli Gianni Lisini Fabio Dell'Acqua Raul Queiroz Feitosa Gilson Alexandre Ostwald Pedro da Costa Paolo Gamba Urban Area Extent Extraction in Spaceborne HR and VHR Data Using Multi-Resolution Features Sensors urban areas multi-resolution processing feature fusion |
| title | Urban Area Extent Extraction in Spaceborne HR and VHR Data Using Multi-Resolution Features |
| title_full | Urban Area Extent Extraction in Spaceborne HR and VHR Data Using Multi-Resolution Features |
| title_fullStr | Urban Area Extent Extraction in Spaceborne HR and VHR Data Using Multi-Resolution Features |
| title_full_unstemmed | Urban Area Extent Extraction in Spaceborne HR and VHR Data Using Multi-Resolution Features |
| title_short | Urban Area Extent Extraction in Spaceborne HR and VHR Data Using Multi-Resolution Features |
| title_sort | urban area extent extraction in spaceborne hr and vhr data using multi resolution features |
| topic | urban areas multi-resolution processing feature fusion |
| url | http://www.mdpi.com/1424-8220/14/10/18337 |
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