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Proceedings Paper

Pattern recognition using maximum likelihood estimation and orthogonal subspace projection
Author(s): M. M. Islam; M. S. Alam
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Paper Abstract

Hyperspectral sensor imagery (HSI) is a relatively new area of research, however, it is extensively being used in geology, agriculture, defense, intelligence and law enforcement applications. Much of the current research focuses on the object detection with low false alarm rate. Over the past several years, many object detection algorithms have been developed which include linear detector, quadratic detector, adaptive matched filter etc. In those methods the available data cube was directly used to determine the background mean and the covariance matrix, assuming that the number of object pixels is low compared to that of the data pixels. In this paper, we have used the orthogonal subspace projection (OSP) technique to find the background matrix from the given image data. Our algorithm consists of three parts. In the first part, we have calculated the background matrix using the OSP technique. In the second part, we have determined the maximum likelihood estimates of the parameters. Finally, we have considered the likelihood ratio, commonly known as the Neyman Pearson quadratic detector, to recognize the objects. The proposed technique has been investigated via computer simulation where excellent performance has been observed.

Paper Details

Date Published: 30 August 2006
PDF: 7 pages
Proc. SPIE 6311, Optical Information Systems IV, 63110Y (30 August 2006); doi: 10.1117/12.679642
Show Author Affiliations
M. M. Islam, Univ. of South Alabama (United States)
M. S. Alam, Univ. of South Alabama (United States)

Published in SPIE Proceedings Vol. 6311:
Optical Information Systems IV
Bahram Javidi; Demetri Psaltis; H. John Caulfield, Editor(s)

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