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gremlin97
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RemoteSensingCorpus

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3) those based on the adaptation of the classifier trained on the source domain, and 4) those based on limited but
effective sampling of labeled samples in the target domain.
With practical examples, we have provided the reader with a thorough introduction to the field and some guidelines
for the selection of the approaches to use in real applica -
tion scenarios.
We believe that DA is of the highest importance to fu -
ture Earth observation since multimodality and repeated
imaging have become unavoidable [ 7]. The data are already
there, and new, challenging problems can now be tackled
with remote sensing. The discipline has succeeded in enter -
ing many new sectors of society, and it is now time to pro -
vide the tools to the users to perform a trustable monitor -
ing that can be obtained in different sensor configurations
or modalities. We think that DA and machine learning in general can contribute to providing an answer to this call.
autHoR in FoRmation
Devis t uia (devis.tuia@geo.uzh.ch) received a diploma in
geography at the University of Lausanne (UNIL) in 2004,
a master of advanced studies degree in environmental en-
gineering at the Federal Institute of Technology of Laus-anne (EPFL) in 2005, and a Ph.D. degree in environmental
sciences at UNIL in 2009. Subsequently, he worked as a
visiting postdoctoral researcher at the University of Valen-cia, Spain, and at the University of Colorado, Boulder. He
then worked as a senior research associate at EPFL under a
Swiss National Foundation (SNF) program. Since 2014, he has been an SNF assistant professor in the Department of
Geography at the University of Zurich. His research inter-
ests include the development of algorithms for informa-tion extraction and data fusion of remote sensing images
using machine-learning algorithms. He serves as chair of
the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. He is Da iS a R iSing F ieLD o F
inVeStigation in Remote
SenSing, aS it an SweRS
tHe nee D FoR ReuSing
aVaiLaBLe gRounD
ReFeRence Samp LeS to
cLaSSiFY o R FuRtHeR
pRoceSS new image
acQuiSitionS t Hat
maY Be coVeRing
DiFFeRent aReaS.
Authorized licensed use limited to: ASU Library. Downloaded on March 08,2024 at 03:13:37 UTC from IEEE Xplore. Restrictions apply.
june 2016 ieee Geoscience and remote sensin G ma Gazine 55
an associate editor of IEEE Journal of Selected Topics in Ap-
plied Earth Observation and Remote Sensing. He is a Senior
Member of the IEEE.
claudio persello (c.persello@utwente.nl) received the
Laurea (B.S.) and Laurea Specialistica (M.S.) degrees in telecommunications engineering and the Ph.D. degree in
communication and information technologies from the University of Trento, Italy, in 2003, 2005, and 2010, respec -
tively. He is currently an assistant professor in the Faculty of Geo-Information Science and Earth Observation, Uni -
versity of Twente, The Netherlands. In 2011–2014, he was
a Marie Curie research fellow, conducting research activity
at the Max Planck Institute for Intelligent Systems and the Remote Sensing Laboratory of the University of Trento. His
main research interests are the analysis of remote sensing
data, machine learning, image classification, pattern recog -
nition, and unmanned aerial vehicles. He is a referee for
multiple journals, including IEEE Transactions on Geoscience
and Remote Sensing , Pattern Recognition Letters , and Image and
Vision Computing Journal . He served on the Scientific Com -
mittee of the Sixth International Workshop on the Analysis of Multitemporal Remote Sensing Images. He is a Member of the IEEE.
Lorenzo Bruzzone (lorenzo.bruzzone@disi.unitn.it)
received the Laurea (M.S.) degree in electronic engineering
(summa cum laude) and the Ph.D. degree in telecommu -
nications from the University of Genoa, Italy, in 1993
and 1998, respectively. He is currently a full professor of
telecommunications at the University of Trento, Italy. His
current research interests are in the areas of remote sens -
ing, radar and synthetic aperture radar, signal processing,
and pattern recognition. He is the principal investigator of the Radar for Icy Moon Exploration instrument in the framework of the Jupiter Icy moons Explorer mission of
the European Space Agency. He is the author (or coauthor)
of 180 scientific publications in refereed international journals (129 in IEEE journals), more than 250 papers in
conference proceedings, and 20 book chapters. He is the
editor-in-chief of IEEE Geoscience and Remote Sensing Maga -
zine and is an associate editor of other journals. He is a
Fellow of the IEEE.
ReFeRence S
[1] R. de Jong, S. de Bruin, A. de Wit, M. E. Schaepman, and D. L.
Dent, “Analysis of monotonic greening and browning trends
from global NDVI time-series,” Remote Sens. Environ. , vol. 115,
no. 2, pp. 692–702, 2011.
[2] F. Pacifici, N. Longbotham, and W. Emery, “The importance of
physical quantities for the analysis of multitemporal and mul -
tiangular optical very high spatial resolution images,” IEEE
Trans. Geosci. Remote Sens. , vol. 52, no. 10, pp. 6241–6256,
2014.
[3] J. J. Walker, K. M. de Beurs, R. H. Wynne, and F. Gao, “Evalu -
ation of Landsat and MODIS data fusion products for analysis
of dryland forest phenology,” Remote Sens. Environ. , vol. 117,
pp. 381–393, Feb. 2012. [4] W. Liao, X. Huang, F. Van Collie, A. Gautama, W. Philips, H. Liu,
T. Zhu, M. Shimoni, G. Moser, and D. Tuia, “Processing of ther -
mal hyperspectral and digital color cameras: Outcome of the 2014 data fusion contest,”
IEEE J. Sel. Topics Appl. Earth Observ. ,
vol. 8, no. 6, pp. 2984–2996, 2015.
[5] J. Amorós-López, L. Gómez-Chova, L. Alonso, L. Guanter, R. Zurita-Milla, J. Moreno, and G. Camps-Valls, “Multitemporal
fusion of Landsat/TM and ENVISAT/MERIS for crop monitor -
ing,”
Int. J. Appl. Earth Obs. Geoinfo. , vol. 23, pp. 132–141,
Aug. 2013.
[6] I. Walde, S. Hese, C. Berger, and C. Schmullius, “From land cov -
er-graphs to urban structure types,” Int. J. Geogr. Info. Sci. , vol.
28, no. 3, pp. 584–609, 2014.
[7] L. Gómez-Chova, D. Tuia, G. Moser, and G. Camps-Valls, “Mul -
timodal classification of remote sensing images: A review and future directions,”
Proc. IEEE , vol. 103, no. 9, pp. 1560–1584,