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"
 
"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
 
      
        
          |  |  |  |  |  | 
        
          | Devis Tuia |  | Rémi Flamary |  | Nicolas Courty | 
      
 
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. 
 
Derek Lichti
Editor-in-Chief
ISPRS Journal of Photogrammetry and Remote Sensing,
The University of Calgary, Canada
E-mail address: ddlichti@ucalgary.ca