Reducing the Dimensionality of Hyperspectral Data using Diffusion Maps

We examine the analysis of hyperspectral data produced by the Hyperspectral Core Imager of AngloGold Ashanti. The dimension of the data is reduced using diffusion maps and the data is then clustered using three divisive clustering strategies. Divisive k-means, PDDP and the NCut algorithm are used. It is shown that the clusterings produced are reasonably accurate compared to a reference clustering, but superior with respect to an internal quality evaluation. Moreover, using a divisive algorithm makes it possible to keep track of inter-cluster similarities. It is also shown that by embedding sample spectra in a dataset it is possible to identify particular minerals within the cluster.

• Type: Journal
• Year: 2009
• Where: IGARSS
• Authors: Luis du Plessis, Steven Damelin, Michael Sears
• Keywords: Divisive Clustering Strategies, Divisive K-means, Hyperspectral Core Imager, IGARSS 2009, Remote Sensing