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Proceedings Paper

Roughness estimation of inhomogeneous paint based on polarization imaging detection
Author(s): Ying Zhang; Jiabin Xuan; Huijie Zhao; Bo Jia; Liyi Luo; Yi Zhang
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Paper Abstract

Polarimetric imagery is a powerful tool in remote sensing because the polarization information of the targets contains surface features, shape, material composition, and surface roughness, which improves applications such as target detection, shape extraction and anomaly detection. For the importance of the quantitative polarmetric remote sensing, the quantitative estimation of the physical characteristics of the target has attracted considerable scientific interests and become the trend of polarimetric imagery. However, because of the spatial scale effect, specifically, in the large detection distance, the quantitative estimation accuracy of the target can be affected by the inhomogeneous target. In this paper, a novel method based on polarization imaging detection to estimate the roughness of inhomogeneous paint surface in outdoor is proposed. A shadowing method was used to eliminate the effect of skylight and improve the estimation accuracy in outdoor experiment. In addition, the correction method based on local variance of roughness distribution was performed to improve the estimation accuracy of inhomogeneous paint. The results showed that the estimation error of roughness for homogeneous paint in two distances were both below 8%. Especially for the target with smaller roughness, after the correction, the estimation accuracy of inhomogeneous paint were below 6% in two detection distances, which confirms the effectiveness of estimation approach and verifies the practicality of the correction method to improve the estimation accuracy of inhomogeneous target in polarimetric imaging remote sensing detection. The approach presented in this paper has important significance for the development of the quantitative remote sensing, especially for targets with inhomogeneous surface.

Paper Details

Date Published: 12 December 2018
PDF: 6 pages
Proc. SPIE 10849, Fiber Optic Sensing and Optical Communication, 1084910 (12 December 2018); doi: 10.1117/12.2505479
Show Author Affiliations
Ying Zhang, Beihang Univ. (China)
Jiabin Xuan, Beihang Univ. (China)
Huijie Zhao, Beihang Univ. (China)
Bo Jia, Beihang Univ. (China)
Liyi Luo, Beihang Univ. (China)
Yi Zhang, Beihang Univ. (China)

Published in SPIE Proceedings Vol. 10849:
Fiber Optic Sensing and Optical Communication
Jie Zhang; Songnian Fu; Qunbi Zhuge; Ming Tang; Tuan Guo, Editor(s)

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