Volume & Issue no: Volume 3, Issue 10, October 2014
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Title: |
Dimensionality Reduction Techniques for
Hyperspectral Images
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Author Name: |
Shraddha P. Lodha and Prof. S. M. Kamlapur |
Abstract: |
ABSTRACT
Hyperspectral Imaging produces an image where each pixel is having narrow spectral bands with plentiful spectral
information. Spectral bands refer to the large number of measured wavelengths bands of Electromagnetic Spectrum. The large
number of spectral bands in hyperspectral data increases the computational burden. So, dimensionality reduction through
spectral feature selection thoroughly affects the accuracy of the given task. A fuzzy rough set is an approach that deals with the
concepts of vagueness and indiscernibility. It finds feature subsets preserving the semantics of the given datasets. Therefore,
this paper proposes the applicability of Fuzzy-Rough Set Approach to select the most significant spectral features from the
hyperspectral data. Selected features are employed to build a more easy and understandable learning model in order to improve
the classification quality of hyperspectral images.
Keywords: Dimensionality Reduction, Fuzzy-Rough Sets, Hyperspectral Imaging, Spectral features |
Cite this article: |
Shraddha P. Lodha and Prof. S. M. Kamlapur , "
Dimensionality Reduction Techniques for
Hyperspectral Images
" , International Journal of Application or Innovation in Engineering & Management (IJAIEM) ,
Volume 3, Issue 10, October 2014 , pp.
092-099 , ISSN 2319 - 4847.
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