A Study of Unsupervised Classification Techniques for Hyperspectral Datasets

Abstract

This work extensively studies and analyses several unsupervised clustering methods for hyperspectral data. We look at unsupervised classification solutions that accomplish adaptive cluster formation in anticipation for new data discoveries. We provide qualitative and quantitative answers to significant problems like high-dimensionality of hyperspectral datasets, multiple sources and relative amounts of existing noise in data and low class separability. The effectiveness of various clustering techniques is illustrated on diverse hyperspectral datasets by intensive experimentation, comparison between techniques and analysis.