J
Jocelyn Chanussot
Researcher at University of Grenoble
Publications - 703
Citations - 39402
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 receiving 27949 citations. Previous affiliations of Jocelyn Chanussot include German Aerospace Center & University of Savoy.
Papers
More filters
Journal ArticleDOI
Hyperspectral Classification Through Unmixing Abundance Maps Addressing Spectral Variability
Edurne Ibarrola-Ulzurrun,Lucas Drumetz,Javier Marcello,Consuelo Gonzalo-Martin,Jocelyn Chanussot +4 more
TL;DR: It was demonstrated that advanced unmixing techniques are needed to address the spectral variability to get accurate abundances estimations and avoided the curse of dimensionality problem found in HSI.
Journal ArticleDOI
Multiple Feature Kernel Sparse Representation Classifier for Hyperspectral Imagery
TL;DR: This paper integrates kernel principal component analysis into multifeature-based KSRC and proposes a novel multiple feature kernel sparse representation-based classifier (namely, MFKSRC) for hyperspectral imagery that outperforms the state-of-the-art classifiers.
Posted Content
Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction
TL;DR: A novel linearized sub space analysis technique with spatial–spectral manifold alignment is developed for a semisupervised hyperspectral dimensionality reduction (HDR), called joint and progressive subspace analysis (JPSA).
Proceedings ArticleDOI
Gradient Optimization for multiple kernel's parameters in support vector machines classification
TL;DR: In this work a gradient descent based algorithm is used to estimate the parameters of kernels with multiple parameters for support vector machines (SVM), with the purpose of classifying hyperspectral remote sensing data.
Journal ArticleDOI
A Physics-Based Unmixing Method to Estimate Subpixel Temperatures on Mixed Pixels
TL;DR: A new algorithm for the analysis of linear spectral mixtures in the thermal infrared domain, with the goal to jointly estimate the abundance and the subpixel temperature in a mixed pixel, i.e., to estimate the relative proportion and the temperature of each material composing the mixed pixel.