Jocelyn Chanussot
University of Grenoble
713 Papers
4.8K Citations
Jocelyn Chanussot is an academic researcher from University of Grenoble. The author has contributed to research in topics: Hyperspectral imaging & Computer science. The author has an hindex of 73, co-authored 614 publications. Previous affiliations of Jocelyn Chanussot include German Aerospace Center & University of Savoy.
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Papers
Pansharpening Based on Deconvolution for Multiband Filter Estimation
TL;DR: A preliminary pansharpened image is exploited to estimate the spatial filter used for detail extraction associated with each spectral band and it is demonstrated that the proposed method outperforms the state-of-the-art FE approaches by employing data sets acquired by the IKONOS, the Quickbird, and the WorldView-3 sensors.
On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation
Saïd Moussaoui,Hafrun Hauksdottir,Frédéric Schmidt,Christian Jutten,Jocelyn Chanussot,David Brie,Sylvain Douté,Jon Atli Benediktsson +7 more
TL;DR: A combination of spatial ICA with spectral Bayesian positive source (BPSS) with a rough classification of pixels is proposed, which allows selection of small, but relevant, number of pixels for the component extraction and consequently the endmember classification.
Segmentation and Classification of Hyperspectral Data using Watershed
Yuliya Tarabalka,Jocelyn Chanussot,Jon Atli Benediktsson,Jesús Angulo,Mathieu Fauvel +4 more
- 07 Jul 2008
TL;DR: The integration of the spatial information from the watershed segmentation into the HS image classification improves the classification accuracies, when compared to the pixel-wise classification.
SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image Classification
TL;DR: This work proposes an efficient symmetric graph metric learning (SGML) framework by incorporating metric learning into the SSGCN paradigm and demonstrates that the classification capacity of the proposed SGML can surpass the comparators on three benchmark data sets.
A time-distributed phase space histogram for detecting transient signals
Florin-Marian Birleanu,Cornel Ioana,Alexandru Serbanescu,Jocelyn Chanussot +3 more
- 22 May 2011
TL;DR: A time-distributed phase space histogram method for detecting burst-type signals in noisy environments.