Building Change Detection Using High Resolution Remotely Sensed Data and GIS

Autor(en): Sofina, Natalia
Ehlers, Manfred
Stichwörter: 2010 HAITI EARTHQUAKE; Change detection; CLASSIFICATION; DAMAGE ASSESSMENT; data mining; Engineering; Engineering, Electrical & Electronic; generation of features; geographic information systems (GIS); Geography, Physical; GIS geographic resources analysis support system (GRASS); IMAGE; Imaging Science & Photographic Technology; open source software; Physical Geography; Remote Sensing; SATELLITE
Erscheinungsdatum: 2016
Herausgeber: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Journal: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volumen: 9
Ausgabe: 8, SI
Startseite: 3430
Seitenende: 3438
Zusammenfassung: 
Remote sensing technology is increasingly being used for rapid detection and visualization of changes caused by catastrophic events. This paper presents a semi-automated feature-based approach to the identification of building conditions especially in affected areas using geographic information systems (GIS) and remote sensing information. For image analysis, a new ``detected part of contour'' (DPC) feature is developed for the assessment of building integrity. The DPC calculates a part of the building contour that can be detected in the remotely sensed image. Additional texture features provide information about the area inside the buildings. The effectiveness of the proposed method is proved by high overall classification accuracy for two different study cases. The results demonstrate that the ``map-to-image'' strategy enables extracting valuable information from the remotely sensed image for each individual vector object, thereby being a better choice for change detection within urban areas.
ISSN: 19391404
DOI: 10.1109/JSTARS.2016.2542074

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