Best Paper 2020
The U.V. Helava Award, sponsored by Elsevier B.V. and Leica Geosystems AG, is a prestigious ISPRS Award, which was established in 1996 to encourage and stimulate submission of high quality scientific papers by individual authors or groups to the ISPRS Journal of Photogrammetry and Remote Sensing, to promote and advertise the Journal, and to honour the outstanding contributions of Dr. Uuno V. Helava to research and development in photogrammetry and remote sensing.
The Award is presented to authors of the best paper, published in the ISPRS Journal during the four-year period. The Award consists of a monetary grant of SFr. 10,000 and a plaque. For each year of the four-year evaluation period, the best paper is selected, and among these four papers, the one to receive the U.V. Helava Award is selected.
The Jury appointed by the ISPRS Council evaluated papers from volumes 159-170 (2020) and announces its decision for the Best Paper. The winner of the 2020 Best Paper Award is:
"Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion".
by Andrea Meraner a, Patrick Ebel b, Xiao Xiang Zhu b,c and Michael Schmitt d
a Data Science in Easrth Observation, Technical University of Munich, Germany
b Data Science in Earth Observation, Technical University of Munich, Germany
c Remote Sensing Technology Institute, German Aerospace Center (DLR), Germany
d Data Science in Earth Observation, Technical University of Munich, Germany
||Xiao Xiang Zhu
published in volume 166, August 2020, pp. 333-346,
Jury's rationale for the paper selection
This paper developed a cloud-removal model based on a deep residual neural network using SAR data and fusion of SAR and optical data. The methodology was tested by using the data across the globe and the seasons. The Jury thinks that the work is truly innovative. The developed methodology tackled a common problem in optical remote sensing. Therefore, it very deserves the best paper award for 2020.
On behalf of the ISPRS and the U.V. Helava Award Jury, I would like to congratulate the authors for this distinction and thank them for their contribution. I would also like to thank the sponsors of the Award, and the Jury members for their thorough evaluations.
Qihao Weng, Ph.D., IEEE/AAAS/ASPRS Fellow
ISPRS Journal of Photogrammetry and Remote Sensing,
Indiana State University, U.S.A.
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