Share Email Print
cover

Proceedings Paper

Research on IKONOS shadow extraction in urban region based on the principal component fusion information distort
Author(s): Cunjun Li; Jihua Wang; Qian Wang; Wenjiang Huang; Xingang Xu
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Shadow exists obviously in high resolution remote sensing images. Automatic extracting shadow is quite important for removing shadow as noise or for mining shadow information. A new method of IKONOS shadow extraction in urban region was presented in this paper based on the principal component (PC) fusion information distort. First, the NIR (near infrared) band with more shadow information was selected for shadow extraction, and the information distort of PC fusion was assessed; it was found that shadow was sensitive to difference index. Second, a relative difference index was structured to enhance shadow information, as a result the values of relative difference index in shadow region were higher and the ones in non-shadow region were lower. Third, possible shadow was distinguished from non-shadow by threshold. Finally standard deviation was used to differentiate shadow from water for possible shadow, and the shadow was extracted. The results show that this shadow extraction method was simple with high accuracy, not only the shadow of high building but also that of low trees were all detected.

Paper Details

Date Published: 22 October 2010
PDF: 10 pages
Proc. SPIE 7824, Remote Sensing for Agriculture, Ecosystems, and Hydrology XII, 78241U (22 October 2010); doi: 10.1117/12.865156
Show Author Affiliations
Cunjun Li, China National Engineer Research Ctr. for Information Technology in Agriculture (China)
Jihua Wang, China National Engineer Research Ctr. for Information Technology in Agriculture (China)
Qian Wang, China National Engineer Research Ctr. for Information Technology in Agriculture (China)
Wenjiang Huang, China National Engineer Research Ctr. for Information Technology in Agriculture (China)
Xingang Xu, China National Engineer Research Ctr. for Information Technology in Agriculture (China)


Published in SPIE Proceedings Vol. 7824:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

© SPIE. Terms of Use
Back to Top