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Proceedings Paper

Shadow extraction for urban area based on hyperspherical color sharpening information distortion
Author(s): Qing Guo; Qu Wang; Hongqun Zhang
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Paper Abstract

A shadow extraction method for urban area is presented based on the hyperspherical color transform (HCT) fusion information distortion. We use the near-infrared band of WorldView-2 data to detect the shadow, because the near-infrared band as the long-wave band is more sensitive to shadow comparing to the short-wave band. In the hyperspherical color sharpening (HCS), n input bands are transformed from an n-dimensional Cartesian space to an n-dimensional hyperspherical color space to generate a single intensity component and n-1 angles, and then the intensity component is replaced with the adjusted panchromatic (Pan) image. After HCT, the information amount of the intensity is larger than that of the Pan band. When using the Pan to replace the intensity to get the fused multispectral (MS) image, the information amount is lost. To assess the information distortion of the fusion result, it is found that the shadow is sensitive to the difference index. Hence, the relative difference index is constructed to enhance the shadow information. More specifically, the relative difference index values are made high for shadow area while they are made low for non-shadow area. However, for the original MS image, the digital number values are low for the shadow area while they are high for non-shadow area. Then, by thresholding, the possible shadow area is separated from the non-shadow area. The experimental results show that this shadow extraction method is simple and accurate; not only the shadow of high building but also the little shadows of low trees and between buildings are all detected.

Paper Details

Date Published: 18 October 2016
PDF: 7 pages
Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100040S (18 October 2016); doi: 10.1117/12.2241348
Show Author Affiliations
Qing Guo, Institute of Remote Sensing and Digital Earth (China)
Qu Wang, Guangdong Univ. of Technology (China)
Hongqun Zhang, Institute of Remote Sensing and Digital Earth (China)

Published in SPIE Proceedings Vol. 10004:
Image and Signal Processing for Remote Sensing XXII
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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