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Kernel Entropy Component Analysis for Remote Sensing Image Clustering

Figure 1

Figure 1
Projections extracted by different methods on the (red) cloud versus (black) cloud-free problem (MERIS bands 1 and 8). The RBF kernel was used, and the width parameter was set to the median distance of all training samples.

Figure 2

Figure 2
Averaged and standard deviation (top left) and individual estimated kappa statistic for the three considered cloudy scenes as a function of the number of training samples for different clustering methods.

Figure 3

Figure 3
(First column) Color composite of MERIS FR images over Spain (BR-2003-07-14 and BR-2004-07-14) and France (FR-2005-03-19) and comparison of the ground truth with the clustering maps of the k-means, kernel k-means, and the clustering of the top two KPCA and KECA features Formula$(m = 2)$. Discrepancies with the ground truth are shown in red when proposed methods detect cloud and in yellow when pixels are classified as cloud free.