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
Panchromatic and Hyperspectral Image Fusion: Outcome of the 2022 WHISPERS Hyperspectral Pansharpening Challenge
TL;DR: The 12th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (IEEE WHISPERS 2022) as discussed by the authors was organized to fuse a panchromatic image with hyperspectral data to get a high spatial resolution hypersensor cube with the same spatial resolution of the pan chromatic image while preserving the spectral information of the hypersensor data.
Detection of Anomalies Produced by Buried Archaeological Structures Using Nonlinear Principal Component Analysis Applied to Airborne Hyperspectral Image
TL;DR: The results obtained by the present work demonstrate that the use of the NLPCA technique, compared to previous approaches, emphasizes the ability of airborne hyperspectral images to identify buried structures.
Speeding up Support Vector Machine (SVM) image classification by a kernel series expansion
T. Habib,Jordi Inglada,Gregoire Mercier,Jocelyn Chanussot +3 more
- 12 Dec 2008
TL;DR: A new decomposition scheme of the SVM decision function is proposed, based on using the Taylor series expansion to approximate the kernel function to provide an approximate decision function that provides a trade-off between the classification accuracy and the processing time.
Support Vector Regression for the Estimation of Forest Stand Parameters Using Airborne Laser Scanning
TL;DR: The results show that SVR prediction models are of similar accuracy with multiple-regression models but are more robust regarding the metrics included in the data sets, and preliminary dimension reduction of the data set by principal component analysis generally benefits more to SVR than to multiple regression.
Decision Fusion for the Classification of Urban Remote Sensing Images
TL;DR: In this paper, a general framework for combining information from several individual classifiers in multiclass classification is proposed based on the definition of two measures of accuracy, i.e., the reliability of the information provided by each classifier and the degree of uncertainty of the fuzzy set.