MICLUST: A Clustering Algorithm for High Dimensional Data
Abstract
This paper presents a method for identifying clusters in hyperspectral images. A new mutual information based similarity index named k-MIDI is proposed. This index, k-MIDI is used for hierarchical clustering of random samples from the data. The remaining observations are assigned to the clusters based on their nearest neighbours.
Authors: Namita Jain, Susmita Ghosh, Emmett J. Ientilucci
Published in: IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (2023)