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Proceedings Paper

Water body extraction in urban region from high resolution satellite imagery with near-infrared spectral analysis
Author(s): Lingli Zhao; Hongjing Yu; Lijuan Zhang
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Paper Abstract

Water body information is very important for urban planning and environment improving. Extracting water body information from the satellite images such as MODIS images, Spot images, Radarsat SAR images and LANDSAT TM images has been explored by some researchers. However, extracting water body information from high resolution satellite images is still a problem because of the shade effect of buildings in urban area. The color of the calm water surface is similar to the shadows and it is difficult to distinguish between water body and shadows from satellite imagery. With Near-Infrared Spectral Analysis of IKONOS imagery, we present a new method to extract water body and make distinctions between the water body and the shadows. We employ existing knowledge for extracting water body information by image analysis at first, then the dark objects is extracted by a object-oriented operation from the high resolution satellite image. Near-infrared spectral analysis is developed to remove the building shadows from the dark objects extracted. Two NIR indexes are combined to remove the shadows. The experiments show that the main water body, such as artificial lake and rivers can be extracted effectively from IKONOS imagery with the method presented in this paper.

Paper Details

Date Published: 4 August 2009
PDF: 6 pages
Proc. SPIE 7383, International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, 73833I (4 August 2009); doi: 10.1117/12.835008
Show Author Affiliations
Lingli Zhao, Honghe Univ. (China)
Hongjing Yu, China Institute of Water Resources and Hydropower Research (China)
Lijuan Zhang, China Univ. of Mining & Technology (China)

Published in SPIE Proceedings Vol. 7383:
International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications
Jeffery Puschell; Hai-mei Gong; Yi Cai; Jin Lu; Jin-dong Fei, Editor(s)

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