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Multiresolution manifold learning for classification of hyperspectral data
2007
2007 IEEE International Geoscience and Remote Sensing Symposium
Nonlinear manifold learning algorithms assume that the original high dimensional data actually lie on a low dimensional manifold defined by local geometric distances between samples. Most of the traditional methods have focused only on the spectral distances in calculating the local dissimilarity of samples, whereas in the case of image data, the spatial distribution and localized contextual information of image samples could provide useful information. As a framework for integrating spatial
doi:10.1109/igarss.2007.4423667
dblp:conf/igarss/KimCCTG07
fatcat:x6x432jwandk5im75m7pu7iqca