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Large-scale feature selection with Gaussian mixture models for the classification of high dimensional remote sensing images

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Figure

Fig. 1. Number of parameters η c per class in function of dimension d: η c = d(d + 3)/2 + 1.
Table I summarizes the presented criterion functions and their characteristics. In the following J denotes one criterion from Table I.
Fig. 4. From top to bottom, true color composition, ground-truth and normal- normal-ized DSM of: (a) tile 5_11, (b) tile 5_12 and (c) tile 3_10.
Fig. 5. Criterion evolution (kappa) in function of the number of selected variables for first trial with Aisa dataset with 500 samples by class
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