Object-based detection of destroyed buildings based on remotely sensed data and GIS
Autor(en): | Sofina, N. Ehlers, M. Michel, U. |
Stichwörter: | Change Detection; Computer programming; Data handling; Data import; Data Mining; Earthquakes; Generation of Features; Geographic information; Geographic Information Systems (GIS); GIS GRASS; High level languages; Object based; Object-based methods; Open Source Software; Open systems; Python; Remote Sensing; Remotely sensed data; Remotely sensed images, Buildings; Signal detection, Geographic information systems | Erscheinungsdatum: | 2011 | Journal: | Proceedings of SPIE - The International Society for Optical Engineering | Volumen: | 8181 | Zusammenfassung: | The paper describes an object-based method to detect destroyed buildings as a consequence of an earthquake. The investigation is based on the analysis of remotely sensed raster and vector-based data. The methodology includes three main steps: generation of features defining the states of buildings, classification of building state and data import in GIS. This paper concentrates on the first step of the three, the generation of features. The appropriately selected features are indispensable for the following successful classification. The described methodology is applied to remotely sensed images of areas that had been subject to an earthquake. Our preliminary results confirm the potential of the proposed approach for detection of the building state. The change detection methodology has been developed solely with Open Source Software. GRASS GIS is involved for vector and raster data processing and presentation. Programming languages Python and Bash are used to develop new GRASS-modules. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE). |
Beschreibung: | Conference of Earth Resources and Environmental Remote Sensing/GIS Applications II ; Conference Date: 20 September 2011 Through 22 September 2011; Conference Code:87385 |
ISBN: | 9780819488084 | ISSN: | 0277786X | DOI: | 10.1117/12.898469 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-81755187407&doi=10.1117%2f12.898469&partnerID=40&md5=af72c3ecae307111ac481d5e492bf19f |
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