Multilevel fusion for classification of very high resolution remote sensing images

[8] Mauro Dalla Mura, J´on Atli Benediktsson, and Lorenzo Bruz- zone, “Self-dual Attribute Profiles for the Analysis of Remote Sensing Images,” in Mathematical Morphology and Its

The aim of the study is to compare and assess the efficiency of conventional hyperspectral techniques (dimension reduction, learning and classification methods) to classify

We focus on the combination of multiple supervised classification and clustering results at the output level based on belief functions for three purposes: (1) to improve the accuracy

3) Simulated user annotation: In the experiments, the ground truth for unlabeled regions are used to simulate the user annotations. A similar strategy was adopted in [7, 30], as well

2) Color Coherence Vector (CCV) [34]: This descriptor, like GCH, is recurrent in the literature. It uses an extraction algorithm that classifies the image pixels as “coherent”

Predictive factors of acute rejection after early cyclosporine withdrawal in renal transplant recipients who receive mycophenolate mofetil: results from a

Figure 51 : Thérapie cellulaire basée sur l’utilisation des ASC humaines dans un modèle murin de brûlure Figure 52 : La population de cellules mésenchymateuses humaines décline

For these missions to come, the ratio of spatial resolution between the multispectral set of images and the panchromatic image is 2 or 4, like in the case of SPOT P and XS