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Asian Journal of Research in Social Sciences and Humanities
Year : 2016, Volume : 6, Issue : 8
First page : ( 1871) Last page : ( 1878)
Online ISSN : 2249-7315.
Article DOI : 10.5958/2249-7315.2016.00717.6

A Hybrid Approach of Dimensionality Reduction and Classification for Hyperspectral Imager

Chidambaram S.*, Dr. Sumathi A.**, Sinduja R.**

*Assistant Professor, Department of ECE, Adhiyamaan College of Engineering, Tamilnadu, India

**Professor and Head, Department of ECE, Adhiyamaan College of Engineering, Tamilnadu, India

Online published on 2 August, 2016.

Abstract

Hyperspectral imagery widely used in remote sensing applications provides detailed data for the sensed materials than multispectral imagery. In this image, the identification of every ground surface picture element is represented by its corresponding spectral signatures which are unique but difficult to process as a result of the large volume of information. Traditional classification strategies might not be used along with the dimension reduction technique. This is often as a result of large dimension, that refers to the very fact that the sample size required to estimate a perform of many variables to a given degree of accuracy grows exponentially with the quantity of variables. Principal Component Analysis (PCA) has been the technique of alternative for dimension reduction. However, PCA is computationally extensive and not eliminate anomalies which will be seen at an arbitrary band. The high dimensional nature of the information collected by such sensors not solely will increase procedure complexness however can also degrade classification accuracy. To deal with this issue, dimension reduction (DR) has become a crucial aid to rising classifier potency on these imagery. In this paper, we summarized the hybrid approach of dimensionality reduction of HSI for the purpose of effective classification. The key findings revealed that the classification accuracy has been increased significantly on the application of dimensionality reduction techniques.

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Keywords

Hyperspectral, Multispectral, Dimensionality Reduction, Classification, Accuracy.

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