Morphological image distances for hyperspectral dimensionality exploration using Kernel-PCA and ISOMAP
MOTIVATION AND AIM The application of nonlinear manifold learning for hyperspec-tral image analysis has been widely studied in last years [1, 4]. One of the main ingredients of these data reduction techniques is the distance used to compare the spectral band images. By means of this distance the pairwise similarity matrix is built and then, the matrix is used to explore the intrinsic dimensionality of the hyperspectral image.