Add to ORKGInternational audienceThis paper deals with a problem of reducing the dimension of hyperspectral images using the principal component analysis. Since hyperspectral images are always reduced before any process, we choose to do this reduction by adding spatial information that can be useful then for classification process; to do it we choose to project our data in new spaces thanks mathematical morphology
International audienceKernel based feature extraction method overcomes the curse of dimensionality a...
The purpose of this tutorial is to get familiar with some techniques for the analysis of remote sens...
Les travaux de thèse effectués dans le cadre de la convention Cifre conclue entrele laboratoire de m...
This article deals with the issue of reducing the spectral dimension of a hyperspectral image using ...
International audienceThis paper proposes a framework to integrate spatial information into unsuperv...
International audiencePixel-wise classification in high-dimensional multivariate images is investiga...
International audienceDimensionality reduction (DR) using tensor structures in morphological scale-s...
International audienceIn this study we investigated the classification of hyperspectral data with hi...
We propose a methodological framework to extract spatial features in hyperspectral imaging data and...
Dimensionality reduction (DR) is an important preprocessing step in hyperspectral image applications...
In this dissertation, the general problem of the dimensionality reduction of hyperspectral imagery i...
This dissertation addresses hyperspectral image analysis, a set of techniques enabling exploitation ...
Hyperspectral Image (HSI) classification refers to classifying hyperspectral data into features, whe...
Recent advances in spectral-spatial classification of hyperspectral images are presented in this pap...
International audienceWe present a new method for the spectral-spatial classification of hyperspectr...
International audienceKernel based feature extraction method overcomes the curse of dimensionality a...
The purpose of this tutorial is to get familiar with some techniques for the analysis of remote sens...
Les travaux de thèse effectués dans le cadre de la convention Cifre conclue entrele laboratoire de m...
This article deals with the issue of reducing the spectral dimension of a hyperspectral image using ...
International audienceThis paper proposes a framework to integrate spatial information into unsuperv...
International audiencePixel-wise classification in high-dimensional multivariate images is investiga...
International audienceDimensionality reduction (DR) using tensor structures in morphological scale-s...
International audienceIn this study we investigated the classification of hyperspectral data with hi...
We propose a methodological framework to extract spatial features in hyperspectral imaging data and...
Dimensionality reduction (DR) is an important preprocessing step in hyperspectral image applications...
In this dissertation, the general problem of the dimensionality reduction of hyperspectral imagery i...
This dissertation addresses hyperspectral image analysis, a set of techniques enabling exploitation ...
Hyperspectral Image (HSI) classification refers to classifying hyperspectral data into features, whe...
Recent advances in spectral-spatial classification of hyperspectral images are presented in this pap...
International audienceWe present a new method for the spectral-spatial classification of hyperspectr...
International audienceKernel based feature extraction method overcomes the curse of dimensionality a...
The purpose of this tutorial is to get familiar with some techniques for the analysis of remote sens...
Les travaux de thèse effectués dans le cadre de la convention Cifre conclue entrele laboratoire de m...