Comparative performance of fractal based and conventional methods for dimensionality reduction of hyperspectral data

Although hyperspectral images contain a wealth of information due to its fine spectral resolution, the information is often redundant. It is therefore expedient to reduce the dimensionality of the data without losing significant information content. The aim of this paper is to show that proposed fractal based dimensionality reduction applied on high dimensional hyperspectral data can be proved to be a better alternative compared to some other popular conventional methods when similar classification accuracy is desired at a reduced computational complexity. Amongst a number of methods of computing fractal dimension, three have been applied here. The experiments have been performed on two hyperspectral data sets acquired from AVIRIS sensor. (C) 2013 Elsevier Ltd. All rights reserved.