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Proceedings Paper

Object detection in hyperspectral imagery by using K-means clustering algorithm with pre-processing
Author(s): M. S. Alam; M. I. Elbakary; M. S. Aslan
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Paper Abstract

K-means clustering method has been employed in different applications of data analysis. This paper develops a target detection system using the k-means algorithm including a preprocessing step based on the Euclidean distance. The pre-processing step reduces the computational complexity of the k-means algorithm in case of hyperspectral imagery. After reducing the set of pixels in the background from the data by using the pre-processing step, k-means algorithm is employed to determine the clusters in rest of the image data cube. Having obtained the clustered data, the objects of interest can easily be detected using the known target signature. The proposed clustering algorithm is successfully applied to the real life hyperspectral data sets where the objects of interest can efficiently be detected. The proposed scheme effectively reduces the convergence time of the k-mean algorithm compared to that required by the traditional k-means algorithm.

Paper Details

Date Published: 9 April 2007
PDF: 9 pages
Proc. SPIE 6574, Optical Pattern Recognition XVIII, 65740M (9 April 2007); doi: 10.1117/12.717926
Show Author Affiliations
M. S. Alam, Univ. of South Alabama (United States)
M. I. Elbakary, Univ. of South Alabama (United States)
M. S. Aslan, Univ. of South Alabama (United States)

Published in SPIE Proceedings Vol. 6574:
Optical Pattern Recognition XVIII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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