International Journal of Application or Innovation in Engineering & Management
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ISSN 2319 – 4847
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Call for Paper, Published Articles, Indexing Infromation Dimensionality Reduction Techniques for Hyperspectral Images , Authors : Shraddha P. Lodha and Prof. S. M. Kamlapur, International Journal of Application or Innovation in Engineering & Management (IJAIEM), www.ijaiem.org
Volume & Issue no: Volume 3, Issue 10, October 2014

Title:
Dimensionality Reduction Techniques for Hyperspectral Images
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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