A Method for Automatic Water High Light Detection and Removal in Single UAV Image

YAN Li; ZHOU Jiantong

doi:10.13203/j.whugis20170011

Volume 43 Issue 10

Oct. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(10): 1511-1517, 1602. > DOI: 10.13203/j.whugis20170011

YAN Li, ZHOU Jiantong. A Method for Automatic Water High Light Detection and Removal in Single UAV Image[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10): 1511-1517, 1602. DOI: 10.13203/j.whugis20170011

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A Method for Automatic Water High Light Detection and Removal in Single UAV Image

YAN Li^1,,
ZHOU Jiantong^1, ,

1.
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Funds:

The Special Scientific Research Fund of Land and Resource Public Welfare Profession of China 201511009

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Author Bio:
YAN Li, PhD, professor, specializes in the theories and methods of photogrammetry, remote sensing and LiDAR technology. E-mail: lyan@sgg.whu.edu.cn
Corresponding author:
ZHOU Jiantong, postgraduate. E-mail: ttong0202@163.com
Received Date: June 11, 2017
Published Date: October 04, 2018

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Abstract

Abstract

UAV(unmanned aerial vehicle) is convenient and low cost, water reflection can cause high light in the image and has adverse effect on UAV data processing and the DOM(digital orthophoto map) quality. We propose a method for automatic water high light detection and removal in single UAV image. Firstly, we extracted initial candidate highlight regions using multi-scale threshold detection in a high light component proposed in this paper and used the Grabcut algorithm to optimize them. Next, a decision tree was built according to the characteristics of water high light to eliminate the error detection. Then, high light regions were refined by the high light points nearby. Finally, we modified Criminisi algorithm to remove high light regions. In actual UAV images, the experimental results show that our method can remove the water high light well and is superior to other methods proposed by Mallick, Shen and Yoon in terms of PNSR(peak signal to noise ratio) and SSIM(structural similarity index) parameters and visual evaluation, and improves the DOM quality as well.
- water high light detection,
- water high light selection,
- water high light removal,
- high light component,
- improved Criminisi algorithm

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References

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