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Proceedings Paper

Building shadow detection in quickbird imagery using normalized multi-spectral data based on object-based classification
Author(s): Xin Shen; Xiaodong Zhang; Deren Li; Jingang Hu
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Paper Abstract

Building shadows are very integral parts in the high spatial resolution remote sensing images, especially in urban region. For this reason, shadow detection is a very important step in remote sensing imagery interpretation. Two kinds of method for shadow detection have been developed: color-based and model-based, but the both methods have their disadvantages: the former couldn't differentiate the objects which have similar color property with the shadow; the latter required other data for assistance. An effective approach to building shadow detection in Quickbird multi-spectral imagery is presented in this paper. The method is based on normalized multi-spectral data and object-based classification technology. By analyzing the spectral and geometrical characteristic of shadow region, the procedure is divided into three stages: 1) normalized multi-spectral imagery is obtained, by using 4 single band image data divide the sum of the four band data (similar with the normalized rgb); 2) using the blue and near infrared band of the normalized multi-spectral imagery to determine a threshold to extract shadow candidate regions; 3) an information integration stage confirmed or rejected each detected candidate image object by using object-based classification technology. Experimental results show that the proposed algorithm is efficient in detecting building shadow for Quickbird imagery.

Paper Details

Date Published: 3 November 2005
PDF: 6 pages
Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 60430Q (3 November 2005); doi: 10.1117/12.654859
Show Author Affiliations
Xin Shen, Wuhan Univ. (China)
Xiaodong Zhang, Wuhan Univ. (China)
Deren Li, Wuhan Univ. (China)
Jingang Hu, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 6043:
MIPPR 2005: SAR and Multispectral Image Processing
Liangpei Zhang; Jianqing Zhang; Mingsheng Liao, Editor(s)

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