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Proceedings Paper

A new thin cloud removal algorithm in single airborne image
Author(s): Jing Wang; Junyong Fang; Xue Liu; Dong Zhao; Qing Xiao
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Paper Abstract

The application of high-resolution airborne images becomes more and more extensive. However because of the complexity of atmospheric environment, airborne remote sensing imaging process is easily affected by cloud and mist, which results in airborne image blurred or loss of information. So it is a necessary task to remove effects of cloud to get clearer images before the next application such as image registration. This paper proposes a new method of removing thin cloud cover from single airborne image. This method applies scale space transform to get scale space sequence images. Then we use difference between different levels to extract cloud area. Next, we use gray classification which represents cloud effect degree in the highest level of cloud area. Finally, we use the original image filtered by Laplacian to subtract the last step result. Compared with other thin cloud cover removal methods which include the homomorphic filtering method, wavelet transform method and mathematical morphology by visual evaluation and statistical analysis, the method proposed by this paper proves to be the most efficient way in the processing of thin cloud cover of airborne image.

Paper Details

Date Published: 24 November 2014
PDF: 6 pages
Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 930111 (24 November 2014); doi: 10.1117/12.2070934
Show Author Affiliations
Jing Wang, Institute of Remote Sensing and Digital Earth (China)
Junyong Fang, Institute of Remote Sensing and Digital Earth (China)
Xue Liu, Institute of Remote Sensing and Digital Earth (China)
Dong Zhao, Institute of Remote Sensing and Digital Earth (China)
Qing Xiao, Institute of Remote Sensing and Digital Earth (China)


Published in SPIE Proceedings Vol. 9301:
International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition
Gaurav Sharma; Fugen Zhou; Jennifer Liu, Editor(s)

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