Best Paper 2015
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, 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, written in English and published exclusively in the ISPRS Journal during the four-year period from January of a Congress year, to December of the year prior to the next Congress. The Award consists of a monetary grant of SFr. 10,000 and a plaque. A five-member Jury, comprising experts of high scientific standing, whose expertise covers the main topics included in the scope of the Journal, evaluates the papers. 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.
The fifth U.V. Helava Award will be presented at the 23rd ISPRS Congress in Prague, 12-19 July 2016. The Jury appointed by the ISPRS Council evaluated papers from volumes 99-110 (2015) and announces its decision for the Best Paper. The winner of the 2015 Best Paper Award is:
"Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions"
by Devis Tuia1, Rémi Flamary2 and Nicolas Courty3
1 University of Zurich, Switzerland
2 Université de Nice Sofia Antipolis, France
3 Université de Bretagne Sud/IRISA, France
published in volume 105, July 2015, pp. 272-285, http://dx.doi.org/10.1016/j.isprsjprs.2015.01.006
Jury's rationale for the paper selection
This well-written paper focuses on the important topic of hyperspectral image analysis. The Jury was impressed by the strong methodology, finding the performance-based feature selection technique to be highly innovative and tractable. They felt this contribution represents a genuine step forward in image classification and, therefore, very deserving of the best paper award for 2015.
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.
ISPRS Journal of Photogrammetry and Remote Sensing,
The University of Calgary, Canada
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