This work was supported by the French State through the French National Research Agency (ANR) in the framework of the “Investments for the future” Programme IdEx Bordeaux-CPU under Grant ANR-10-IDEX-03-0International audienc
International audienceA new multiple classifier method for spectral-spatial classification of hypers...
A new multiple classifier method for spectral-spatial classi-fication of hyperspectral images is pro...
Abstract—A new method for segmentation and classification of hyperspectral images is proposed. The m...
In this letter, we propose a new version of the rotation forest (RoF) method for the pixelwise class...
International audienceIn this letter, an ensemble learning approach, Rotation Forest, has been appli...
Ensemble learning is widely used to combine varieties of weak learners in order to generate a relati...
International audienceIn this paper, we propose a new spectral-spatial classification strategy to en...
In this thesis, we propose several new techniques for the classification of hyperspectral remote sen...
Decision tree-based Rotation Forest could generate satisfactory but lower classification accuracy fo...
Random Forest (RF) is a widely used classifier to show a good performance of hyperspectral data clas...
In this paper, we discuss about hyperspectral image processing where it plays an important role in r...
International audienceWith different principles, support vector machines (SVMs) and multiple classif...
International audienceA new method for segmentation and classification of hyperspectral images is pr...
Existing remote sensing images of ground objects are difficult to annotate, and building a hyperspec...
Nowadays, the hyperspectral imaging is the focus of intense research, because its applications can b...
International audienceA new multiple classifier method for spectral-spatial classification of hypers...
A new multiple classifier method for spectral-spatial classi-fication of hyperspectral images is pro...
Abstract—A new method for segmentation and classification of hyperspectral images is proposed. The m...
In this letter, we propose a new version of the rotation forest (RoF) method for the pixelwise class...
International audienceIn this letter, an ensemble learning approach, Rotation Forest, has been appli...
Ensemble learning is widely used to combine varieties of weak learners in order to generate a relati...
International audienceIn this paper, we propose a new spectral-spatial classification strategy to en...
In this thesis, we propose several new techniques for the classification of hyperspectral remote sen...
Decision tree-based Rotation Forest could generate satisfactory but lower classification accuracy fo...
Random Forest (RF) is a widely used classifier to show a good performance of hyperspectral data clas...
In this paper, we discuss about hyperspectral image processing where it plays an important role in r...
International audienceWith different principles, support vector machines (SVMs) and multiple classif...
International audienceA new method for segmentation and classification of hyperspectral images is pr...
Existing remote sensing images of ground objects are difficult to annotate, and building a hyperspec...
Nowadays, the hyperspectral imaging is the focus of intense research, because its applications can b...
International audienceA new multiple classifier method for spectral-spatial classification of hypers...
A new multiple classifier method for spectral-spatial classi-fication of hyperspectral images is pro...
Abstract—A new method for segmentation and classification of hyperspectral images is proposed. The m...
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