Pattern recognition using maximum likelihood estimation and orthogonal subspace projection

M. M. Islam; M. S. Alam

doi:10.1117/12.679642

30 August 2006 Pattern recognition using maximum likelihood estimation and orthogonal subspace projection

M. M. Islam, M. S. Alam

Author Affiliations +

Proceedings Volume 6311, Optical Information Systems IV; 63110Y (2006) https://doi.org/10.1117/12.679642
Event: SPIE Optics + Photonics, 2006, San Diego, California, United States

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.

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M. M. Islam and M. S. Alam "Pattern recognition using maximum likelihood estimation and orthogonal subspace projection", Proc. SPIE 6311, Optical Information Systems IV, 63110Y (30 August 2006); https://doi.org/10.1117/12.679642

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KEYWORDS

Sensors

Detection and tracking algorithms

Hyperspectral imaging

Algorithm development

Computer simulations

Hyperspectral simulation

Image sensors

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