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Lpg.obs.ujf-grenoble.fr

Phone: +33 (0)4 76 63 52 84Fax: +33 (0)4 76 51 41 46 Optique remote sensing, hyperspectral imagery, imaging spectrometers, physical model- ing of the remote sensing signal, radiative transfer modeling, Digital Elevation Models,spectroscopy of ices and gases, planetary sciences, machine learning in remote sensing Ph.D., Planetary Sciences (expected graduation date: October 2011) • Area of Study: Remote Sensing in Planetary Sciences• Thesis: Evaluating the potential of statistical and physical methods to analyze multidimensional hyperspectral images of Mars • Specialization: image and signal processing• Thesis: Ensemble methods for classification of remote sensing hyperspectral data • Department of Electrical and Computer Engineering, University of Iceland, • Area of Study: Machine Learning in Remote Sensing Degree in Electrical Engineering, June 2007 • Double degree agreement along with the Universitat Polit` • Thesis: Calibration of Computer-Aided Design tools for parasitic extraction in • Area of Study: Design flow in integrated circuits Degree in Electrical Engineering, June 2006 • Technical assistance in the installation of the telecommunication systems of a Technical Skills processing of high-dimensional data, data visualization, resampling methods, polynomial data fitting, statistics, Fourier transform analysis of multidimensional data, dimensionality reduction, georeferencing, coreg-istration, endmember extraction, spectral unmixing, classification, image processing Operating Systems: Microsoft Windows family, Apple OS X, Linux and Solaris Citizenship: SpanishLanguages: Fluent in English and French. Spanish and Catalan as mother tongues.
e and J. Chanussot. Unsupervised endmember ex- traction: application to hyperspectral images from Mars.
Geoscience and Remote Sensing - Special Issue on Spectral Unmixing of RemotelySensed Images, in preparation, 2011 e. Spectral Smile Correction of CRISM/MRO Hyperspectral Images. IEEE Transactions on Geoscience and Remote Sensing - Hyperspectral imageand Signal Processing Special Issue, to appear, 2010 X. Ceamanos, B. Waske, J. A. Benediktsson, J. Chanussot, M. Fauvel and J. R.
Sveinsson. A Classifier Ensemble Based on Fusion of Support Vector Machines forClassifying Hyperspectral Data. International Journal of Image and Data Fusion, toappear, 2010.
e. Calibration of CRISM/MRO apparent wavelengths using synthetic data. 2nd IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. WHISPERS’10, June 2010, Reykjavik, Iceland.
e and X. Ceamanos. Retrieving Mars aerosol optical depth from CRISM/MRO imagery. WHISPERS’10, June 2010, Reykjavik, Iceland.
e and J. Chanussot. Martian aerosol abundance es- timation based on unmixing of hyperspectral imagery. WHISPERS’10, June 2010,Reykjavik, Iceland.
e. Spectral smile correction in CRISM hyperspectral images.
1st IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution inRemote Sensing. WHISPERS’09, August 2009, Grenoble, France.
e and X. Ceamanos. Unsupervised endmember extrac- tion of Martian hyperspectral images. WHISPERS’09, August 2009, Grenoble.
e. Spectral smile correction in CRISM hyperspectral images.
American Geophysical Union Fall Meeting, December 2009, San Francisco, USA.
X. Ceamanos, B. Waske, J. A. Benediktsson, J. Chanussot and J. R. Sveinsson. En- semble strategies for classifying hyperspectral remote sensing data. 8th InternationalWorkshop on Multiple Classifier Systems, June 2009, Reykjavik, Iceland.
J. A. Benediktsson, X. Ceamanos, B. Waske, J. Chanussot, J. R. Sveinsson and M.
Fauvel. Ensemble methods for classification of hyperspectral data. InternationalGeoscience & Remote Sensing Symposium. IGARSS’08, July 2008, Boston, USA.
Technical reviewer and volunteer of the IEEE Workshop on Hyperspectral Image andSignal Processing: Evolution in Remote Sensing, 2009 and 2010

Source: http://lpg.obs.ujf-grenoble.fr/homepages/IMG/pdf/ceamanosCV_2010-4.pdf

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homepages.abdn.ac.uk

Abstract. The past ten years have shown a great variety of approaches for formal argumentation. An interesting question is to which extent these various formalisms correspond to the different application domains. That is, does the appropriate argu- mentation formalism depend on the particular domain of application, or does “one size fits all”. In this paper, we study this question from the p

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