5 February 2004 Detection of buildings through automatic extraction of shadows in Ikonos imagery
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Abstract
The extraction of man-made objects from remotely sensed imagery is a common application in remote sensing. Building detection is useful in territorial planning, mapping and Geographic Information Systems. Nevertheless these features are difficult to recognise in satellite data because of their variations in structure and size and especially because of the spatial resolution of the imagery. IRS panchromatic data, with 5,8 meters pixel size, was the higher spatial resolution sensor in civil applications until the Ikonos imageries distribution. Several approaches have been proposed for building detection in aerial images. Buildings cast a shadow in some direction and that is why many authors have employed shadows to detect constructions. Other authors use shadows to verify them, once they have been detected by some other techniques. This work focus on shadows detection probabilistic methods: it is found that digital supervised classification of the first principal component obtained from the application of a principal component analysis on the four channels of Ikonos allows identifying shadows and distinguishing them from other covers in the image. It is a fast and effective method and it can be implemented through tools available in commercial remote sensing software. This shadow detection system will provide cost-effectiveness in the inventorying of buildings, especially in areas of dispersed settlement, given that it significantly reduces fieldwork, and even can function as a support and test of the methods of automatic extraction of buildings from satellite images developed up to now.
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Julia Armesto Gonzalaz, Mariluz Luz Gil Docampo, Ignacio Canas Guerrero, "Detection of buildings through automatic extraction of shadows in Ikonos imagery", Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); doi: 10.1117/12.510023; https://doi.org/10.1117/12.510023
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