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Warianty tytułu
Języki publikacji
Abstrakty
Classification of roofing materials with the use of high resolution satellite imagery is a difficult issue, especially due to the fact that roofs are characterised by large diversity of shapes and textures, mainly caused by different roof surfaces illumination. To automate the process of roofing material types classification the influence of diversified illumination of individual roof surfaces should be eliminated. Topographic correction of satellite imagery may decrease influence of such effects and therefore leads to more accurate classification results. This paper presents classification results of roofing materials based on an 8-channel WorldView-2 satellite image. The digital terrain model and the digital surface model created with the use of aerial laser scanning data provided by the ISOK project were used for the topographic correction. The accuracy of the supervised classification of WorldView-2 image achieved for asbestos-cement roofing materials was at the level of 76-92%, (depending on the variant of classification). After grouping roofing materials by similar materials (e.g. painted sheet metal and metal tiles) it is possible to achieve classification results with the accuracy of ca. 70-80%.
Rocznik
Tom
Strony
283--298
Opis fizyczny
Bibliogr. 19 poz., rys., tab., wykr., zdj.
Twórcy
autor
- Department of Photogrammetry, Remote Sensing and Spatial Information Systems, Warsaw University of Technology
autor
- Department of Photogrammetry, Remote Sensing and Spatial Information Systems, Warsaw University of Technology
Bibliografia
- ALMEIDA C.M., SOUZA I.M.E., ALVES C.D., PINHO C.M.D., PEREIRA M.N., FEITOSA R.Q. 2007. Multilevel Object-Oriented Classification of QuickBird Images for Urban Population Estimates. 15th ACM International Symposium on Advances in Geographic Information Systems (ACM GIS 2007), Seattle.
- ATTURO C., FIUMI L. 2005. Thermographic analyses for monitoring urban areas in Rome to study Heat Islands. International Conference „Passive and Low Energy Cooling 145 for the Built Environment”, May’ 2005, Santorini, Greece, p. 145-150.
- BASSANI C., CAVALLI R.M., CAVALCANTE F., CUOMO V., PALOMBO A., PASCUCCI S., PIGNATTI S. 2007. Deterioration status of asbestos-cement roofing sheets assessed by analyzing hyperspectral data. Remote Sensing of Environment, 109(2007): 361-378.
- BELGIU M., TOMLJENOVIC I., LAMPOLTSHAMMER T.J., BLASCHKE T., HÖFLE B. 2012. Ontology-Based Classification of Building Types Detected from Airborne Laser Scanning Data. Remote Sensing, 6: 1347-1366, DOI:10.3390/RS6021347.
- CHEN Y., SU W., LI J., SUN Z. 2009. Hierarchical object-oriented classification using very high resolution imagery and LIDAR data over urban areas. Advances in Space Research, 43: 1101-1110.
- COLBY J.D. 1991. Topographic Normalization in Rugged Terrain. Photogrammetric Engineering & Remote Sensing, 57(5): 531-537.
- DINIS J., NAVARRO A., SOARES F., SANTOS T., FREIRE S., FONSECA A., AFONSO N., TENEDÓRIO J. 2010. Hierarchical object-based classification of dense urban areas by integrating high spatial resolution satellite images and LIDAR elevation data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII(4/C7): 6.
- DYCZEK J. 2007. Azbest i materiały zawierające azbest. Ocena ryzyka emisji włókien azbestu. In: Bezpieczne postępowanie z azbestem i materiałami zawierającymi azbest. Ed. J. Dyczek. Wydawnictwo Naukowe „Akapit”, Kraków, p. 7-26.
- FONSECA L., NAMIKAWA L., CASTEJON E., CARVALHO L., PINHO C., PAGAMISSE A. 2011. Image Fusion for Remote Sensing Applications. In: Image Fusion. Ed. O. Ukimura. Publisher: InTech, p. 153-178.
- HEROLD M., SCEPAN J., MÜLLER A., GÜNTHER S. 2002. Object-Oriented Mapping and Analysis of Urban Land Use/Cover Using IKONOS Data. Proceedings of 22nd EARSeL Symposium Geoinformation for European-Wide Integration, Prague, Czech Republic, 4-6 June 2002.
- HEROLD M., GARDNER M., ROBERTS D. 2003. Spectral resolution requirements for mapping urban areas. IEEE Trans. Geoscience Remote Sensing, 419): 1907-1919.
- HOLLAUS M., MANDLBURGER G., PFEIFER N., MÜCKE W. 2010. Land cover dependent derivation of digital surface models from airborne laser scanning data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38: 1-3.
- KURCZYŃSKI Z., BAKUŁA K. 2013. The selection of aerial laser scanning parameters for country wide digital elevation model creation. 13th SGEM GeoConference on Informatics, Geoinformatics and Remote Sensing. SGEM2013 Conference Proceedings, 2: 695-702, DOI:10.5593/SGEM2013/BB2.V2/S10.020.
- OSIŃSKA-SKOTAK K. 2014. Zastosowanie technik teledetekcyjnych do inwentaryzacji cementowo-azbestowych pokryć dachowych. Teledetekcja Środowiska, 51(2): 73-83.
- RICHTER R., KELLENBERGER T., KAUFMANN H. 2009. Comparison of topographic correction methods. Remote Sensing, 1: 184-196, DOI:10.3390/RS1030184.
- ROBERTS D.A., HEROLD M. 2004. Imaging spectrometry of urban materials. In: Infrared Spectroscopy in Geochemistry, Exploration and Remote Sensing. Eds. P. King,, M.S. Ramsey, G. Swayze. The Mineralogical Association of Canada, Short Course Series, 33: 155-181.
- STORY M., CONGALTON R. G. 1986. Accuracy assessment: A user;s perspective. Photogrammetric Engineering and Remote Sensing, 52: 397-399.
- SZESZENIA-DĄBROWSKA N., SOBALA W. 2010. Zanieczyszczenie środowiska azbestem. Skutki zdrowotne. Raport z badań, Instytut Medycyny Pracy im. prof. J. Nofera, Oficyna Wydawnicza MA, Łódź.
- VALERO S., CHANUSSOT C., GUEGUEN P. 2008. Classification of basic roof types based on VHR optical data and digital elevation model. IGARSS’ 2008, Commission IV, p. 149-152.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3c2f296d-45bb-44f7-98cf-2a276a2deec3