IEEE Geoscience and Remote Sensing Magazine - March 2018 - 54

Rocquencourt, France, in 2003. He is a research scientist in
the Information Processing and Systems Department at the
Office National d'Etudes et de Recherches Aérospatiales, the
French Aerospace Laboratory
in Palaiseau. His research objective is visual understanding by means of data-driven
TO MAxIMIZE IMPACT
techniques. His focus is on
AND PROMOTE THE
developing machine-learning
POTENTIAL OF CURRENT
and deep-learning methods
MULTISOURCE REMOTEfor remote sensing, (flying) roSENSING TECHNOLOGIES,
botics, and three-dimensional
THE OPEN-ACCESS OPTION
vision. He teaches machine
WILL bE USED FOR
learning, image processing,
THIS SUbMISSION.
and computer vision at the
Institut d'Optique Graduate School, École Nationale
Supérieure de Techniques
Avancées ParisTech, and École Polytechnique, Palaiseau,
France. He is chair of the IEEE Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. He also has been a research fellow at the
Consiglio Nazionale Delle Ricerche Pisa, Italy; the University
of Bern, Switzerland; and École Normale Supérieure Paris-
Saclay, Cachan, France.
Naoto Yokoya (naoto.yokoya@riken.jp) received his
M.Eng. and Ph.D. degrees in aerospace engineering from
the University of Tokyo in 2010 and 2013, respectively. He
is the leader of the Geoinformatics Unit at the Riken Center
for Advanced Intelligence Project, Tokyo. Previously, he was
a research fellow with the Japan Society for the Promotion
of Science; assistant professor with the University of Tokyo;
and an Alexander von Humboldt research fellow with the
German Aerospace Center, Oberpfaffenhofen, and Technical University of Munich, Germany. His research interests
include image processing and pattern recognition in remote
sensing. He is cochair of the IEEE Geoscience and Remote
Sensing Society (GRSS) Image Analysis and Data Fusion
Technical Committee and secretary of the IEEE GRSS All
Japan Joint Chapter. He is a Member of the IEEE.
Ronny Hänsch (r.haensch@tu-berlin.de) received his
undergraduate degree in computer science and his Ph.D.
degree in engineering from the Technische Universität Berlin, Germany, in 2007 and 2014, respectively. His research
interests include computer vision, machine learning, object
detection, neural networks, and random forests. He worked

in the field of object detection and classification from remote-sensing images with a focus on polarimetric synthetic
aperture radar images. His recent research interests focus on
ensemble methods for image analysis as well as the development of probabilistic methods for three-dimensional reconstruction by structure from motion and multiview stereo.
Saurabh Prasad (saurabh.prasad@ieee.org) received his
B.S. degree in electrical engineering from Jamia Millia Islamia, Delhi, India; his M.S. degree in electrical engineering
from Old Dominion University, Norfolk, Virginia, United
States; and his Ph.D. degree in electrical engineering from
Mississippi State University, Starkville, United States, in
2003, 2005, and 2008, respectively. He is an assistant professor in the Electrical and Computer Engineering Department at the University of Houston, Texas. His research
work involves the design of optimal sparse representation
frameworks, active learning, Bayesian inference, kernel
machines, and deep learning for robust image analysis. His
awards include the Best Student Paper Award at the 2008
IEEE International Geoscience and Remote Sensing Symposium, the State Pride Faculty Award at Mississippi State
University, the NASA New Investigator Award, and the Junior Faculty Research Award at the University of Houston.
He is a reviewer for various journals on signal processing,
image processing, and machine learning and is an associate
editor for IEEE Transactions on Geoscience and Remote Sensing.
He is a Senior Member of the IEEE.
REFERENCES
[1] C. Debes, A. Merentitis, R. Heremans, J. Hahn, N. Frangiadakis,
T. van Kasteren, W. Liao, R. Bellens, A. Pizurica, S. Gautama,
W. Philips, S. Prasad, Q. Du, and F. Pacifici, "Hyperspectral
and LiDAR data fusion: Outcome of the 2013 GRSS data fusion
contest," IEEE J. Select. Topics Appl. Earth Observ. Remote Sensing,
vol. 7, no. 6, pp. 2405-2418, June 2014.
[2] IEEE GRSS. (2018). 2018 IEEE GRSS Data Fusion Contest: Advanced multi-sensor optical remote sensing for urban land use
and land cover classification. [Online]. Available: http://www
.grss-ieee.org/community/technical-committees/data-fusion/
data-fusion-contest/
[3] IEEE GRSS. (2018). IEEE Geoscience Remote Sensing Society. LinkedIn. [Online]. Available: https://www.linkedin.com/
groups/IEEE-Geoscience-Remote-Sensing-Society-3678437
[4] IEEE GRSS. (2018). IGARSS 2018 paper submission kit. [Online].
Available: http://www.igarss2018.org/Papers/PaperKit.html
grs

ieee Geoscience and remote sensing magazine

march 2018

http://www.grss-ieee.org/community/technical-committees/data-fusion/data-fusion-contest/ http://www.grss-ieee.org/community/technical-committees/data-fusion/data-fusion-contest/ http://www.grss-ieee.org/community/technical-committees/data-fusion/data-fusion-contest/ https://www.linkedin.com/groups/IEEE-Geoscience-Remote-Sensing-Society-3678437 https://www.linkedin.com/groups/IEEE-Geoscience-Remote-Sensing-Society-3678437 http://www.igarss2018.org/Papers/PaperKit.html

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - March 2018