IEEE Geoscience and Remote Sensing Magazine - June 2016 - 19

computing, and their applications in climate/ecosystems
and biomedical domains. He is the lead principal investigator of a five-year, US$10 million project, "Understanding Climate Change-a Data Driven Approach," funded
by the National Science Foundation's Expeditions in Computing program that is aimed at pushing the boundaries
of computer science research. He has authored more than
300 research articles and has coedited or coauthored ten
books, including two textbooks, Introduction to Parallel
Computing and Introduction to Data Mining, that are used
worldwide and have been translated into many languages.
His foundational research in data mining and its applications to scientific data was honored by the Association for
Computing Machinery (ACM) Special Interest Group on
Knowledge Discovery and Data Mining 2012 Innovation
Award, which is the highest award for technical excellence
in the field of knowledge discovery and data mining. He is
a Fellow of the ACM, the IEEE, and the American Association for the Advancement of Science.
reFereNces
[1] P. Coppin, I. Jonckheere, K. Nackaerts, B. Muys, and E. Lambin,
"Digital change detection methods in ecosystem monitoring: A review," Int. J. Remote Sens., vol. 25, no. 9, pp. 1565-1596, May 2004.
[2] M. C. Hansen and T. R. Loveland, "A review of large area monitoring of land cover change using Landsat data," Remote Sens. Environ., vol. 122, pp. 66-74, July 2012.
[3] A. Hecheltjen, F. Thonfeld, and G. Menz, "Recent advances in
remote sensing change detection-A review," in Land Use and
Land Cover Mapping in Europe. New York: Springer-Verlag, 2014,
pp. 145-178.
[4] J. F. Mas, "Monitoring land-cover changes: A comparison of
change detection techniques," Int. J. Remote Sens., vol. 20, no. 1,
pp. 139-152, 1999.
[5] A. Singh, "Review article digital change detection techniques
using remotely-sensed data," Int. J. Remote Sens., vol. 10, no. 6,
pp. 989-1003, 1989.
[6] R. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, "Image change
detection algorithms: A systematic survey," IEEE Trans. Image Processing, vol. 14, no. 3, pp. 294-307, Mar. 2005.
[7] M. J. Cracknell and A. M. Reading, "Geological mapping using
remote sensing data: A comparison of five machine learning
algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information,"
Comput. Geosci., vol. 63, pp. 22-33, Feb. 2014.
[8] B. W. Szuster, Q. Chen, and M. Borger, "A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones," Appl. Geography, vol. 31, no. 2,
pp. 525-532, Apr. 2011.
[9] J. C. W. Chan, K. P. Chan, and A. G. O. Yeh, "Detecting the nature
of change in an urban environment: A comparison of machine
learning algorithms," Photogramm. Eng. Remote Sens., vol. 67,
no. 2, pp. 213-226, 2001.
[10] G. Camps-Valls, L. Gomez-Chova, J. Munoz-Mari, J. Rojo-Alvarez, and M. Martinez-Ramon, "Kernel-based framework for
multitemporal and multisource remote sensing data classifica-

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tion and change detection," IEEE Trans. Geosci. Remote Sensing,
vol. 46, no. 6, pp. 1822-1835, June 2008.
[11] Land Processes Distributed Active Archive Center. [Online].
Available: http://lpdaac.usgs.gov
[12] California Department of Forestry and Fire Protection, Fire and
Resource Assessment Program. [Online]. Available: http://frap.
fire.ca.gov
[13] T. Pang-Ning, M. Steinbach, V. Kumar, Introduction to Data Mining. New York: Pearson, 2005, p. 74.
[14] G. Camps-Valls and L. Bruzzone, "Kernel-based methods for hyperspectral image classification," IEEE Trans. Geosci. Remote Sensing, vol. 43, no. 6, pp. 1351-1362, June 2005.
[15] A. Boucher, K. Seto, and A. Journel, "A novel method for mapping land cover changes: Incorporating time and space with
geostatistics," IEEE Trans. Geosci. Remote Sensing, vol. 44, no. 11,
pp. 3427-3435, Nov. 2006.
[16] L. Bruzzone and S. B. Serpico, "An iterative technique for the
detection of land-cover transitions in multitemporal remotesensing images," IEEE Trans. Geosci. Remote Sensing, vol. 35, no. 4,
pp. 858-867, July 1997.
[17] W. Li, Q. Guo, and C. Elkan, "A positive and unlabeled learning
algorithm for one-class classification of remote-sensing data,"
IEEE Trans. Geosci. Remote Sensing, vol. 49, no. 2, pp. 717-725,
Feb. 2011.
[18] B. Demir, F. Bovolo, and L. Bruzzone, "Detection of land-cover
transitions in multitemporal remote sensing images with activelearning-based compound classification," IEEE Trans. Geosci. Remote Sensing, vol. 50, no. 5, pp. 1930-1941, May 2012.
[19] P. Griffiths, P. Hostert, O. Gruebner, and S. van der Linden,
"Mapping megacity growth with multi-sensor data," Remote
Sens. Environ., vol. 114, no. 2, pp. 426-439, Feb. 2010.
[20] B. Waske and J. Benediktsson, "Fusion of support vector machines for classification of multisensor data," IEEE Trans. Geosci.
Remote Sensing, vol. 45, no. 12, pp. 3858-3866, Dec. 2007.
[21] K. G. Pillai and R. R. Vatsavai, "Multi-sensor remote sensing image change detection: An evaluation of similarity measures," in
Proc. 2013 IEEE 13th Int. Conf. Data Mining Workshops (ICDMW),
Dallas, TX, pp. 1053-1060.
[22] L. Bruzzone, "Classification of hyperspectral data by continuation semi-supervised SVM," in Proc. IEEE Int. Geoscience and
Remote Sensing Symp., 2007 (IGARSS 2007), Barcelona, Spain,
pp. 3794-3797.
[23] F. Bovolo, L. Bruzzone, and M. Marconcini, "A novel approach to
unsupervised change detection based on a semisupervised SVM
and a similarity measure," IEEE Trans. Geosci. Remote Sensing,
vol. 46, no. 7, pp. 2070-2082, July 2008.
[24] S. Patra, S. Ghosh, and A. Ghosh, "Change detection of remote
sensing images with semi-supervised multilayer perceptron,"
Fundamenta Informaticae, vol. 84, no. 3, pp. 429-442, 2008.
[25] D. Tuia, F. Ratle, F. Pacifici, M. Kanevski, and W. Emery, "Active
learning methods for remote sensing image classification," IEEE
Trans. Geosci. Remote Sensing, vol. 47, no. 7, pp. 2218-2232, July 2009.
[26] D. Tuia, M. Volpi, L. Copa, M. Kanevski, and J. Munoz-Mari, "A
survey of active learning algorithms for supervised remote sensing image classification," IEEE J. Sel. Topics Signal Process., vol. 5,
no. 3, pp. 606-617, June 2011.
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Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - June 2016
