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Proceedings Paper

Shadows detection and removal of IKONOS imagery based on spatial-distribution relations
Author(s): Yingbao Yang; Weizhong Su; Sansheng Cheng; Yanwen Li
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Paper Abstract

How to cull shadows and extract needed information accurately is particularly significant. For major remote sensing applications, it may be preferable that shadows are minimized and the detailed information in high-resolution satellite imagery is clear. Firstly this paper reviews some of basic methods of detecting and removing shadows, and outlines their disadvantages. Then taking Nanjing city as study area, we propose a novel method combing spatial-distribution relation with classification to detect building shadows from IKONOS imagery. When detecting and extracting shadows, a majority index based on neighborhood analysis is provided, and a 5-meter buffer analysis is operated after supervised classification. When removing the shadows, a piecewise linear contrast stretch and histogram match are used. The results show that the accuracy of shadows detection and extraction is 92.3%, but texture analysis is 88.1%, and the detail information within shadows regions is enhanced, and there are no bright edges around shadows regions by applying the techniques developed in this paper.

Paper Details

Date Published: 28 October 2006
PDF: 7 pages
Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 641903 (28 October 2006); doi: 10.1117/12.712589
Show Author Affiliations
Yingbao Yang, Hohai Univ. (China)
Weizhong Su, Nanjing Institute of Geography and Limnology (China)
Chinese Academy of Sciences (China)
Sansheng Cheng, Hohai Univ. (China)
Yanwen Li, Hohai Univ. (China)


Published in SPIE Proceedings Vol. 6419:
Geoinformatics 2006: Remotely Sensed Data and Information
Liangpei Zhang; Xiaoling Chen, Editor(s)

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