IJMTES – METHODS TO REDUCE DIMENSIONALITY FOR ANALYZING HYPERSPECTRAL DATA

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : METHODS TO REDUCE DIMENSIONALITY FOR ANALYZING HYPERSPECTRAL DATA

Author’s Name : K Sunil Manohar Reddyunnamed

Volume 04 Issue 03 2017

ISSN no:  2348-3121

Page no: 84-86

Abstract – The Hyper spectral images provide the images with hundreds of narrow contiguous spectral channels. The spectral information provided by the hyper spectral images is high when compared to the other class of remote sensing images such as panchromatic, multispectral images. Even though the hyper spectral images contain sufficient spectral information but processing and exploiting the hyper spectral data is considered as challenging task because of its high dimensionality. Data redundancy is one of the problems when processing hyper spectral data. And some bands in hyper spectral images are noisy and some bands are set to zero. So when we use all the bands for processing it increases the computational complexity in terms of storage and processing time. Because of this problem Dimensionality reduction techniques must be applied before we use it for processing. With that we can map high dimensionality of hyper spectral image data to lower dimensions without losing much of spectral features provided by the original hyper spectral data cube. Many techniques are developed for this dimensionality reduction; three of those techniques are analyzed in this paper

KeywordsDimensionality Reduction, Hyper spectral, Band Selection, Principal Component Analysis, Minimum Noise Fraction, Independent Component Analysis

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