Target detection in high-dimensional space using a stochastic expectation maximization algorithm

M. Karakaya; M. S. Alam; M. I. Elbakary

doi:10.1117/12.718394

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7 May 2007 Target detection in high-dimensional space using a stochastic expectation maximization algorithm

M. Karakaya, M. S. Alam, M. I. Elbakary

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Proceedings Volume 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII; 656509 (2007) https://doi.org/10.1117/12.718394
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

This paper presents an object detection algorithm based on stochastic expectation-maximization (SEM) algorithm. SEM algorithm is based on the stochastic, expectation, and maximization steps to iteratively estimate the parameters of the classes in many applications including hyperspectral data cube (HDC). However, the application of SEM algorithm for classification of hyperspectral imagery becomes impractical because of the huge amount of data (e.g. 512 x 512 x 220). To avoid this problem, we proposed a preprocessing step for SEM algorithm to fast classify the data cube formulating an Object Detection algorithm based on SEM for detecting small objects in hyperspectral imagery. In the proposed preprocessing step, we utilize the exponential of Euclidian Distance for rapidly separation of data cube into a potential object of interest class and a background class. Then, SEM algorithm is employed to classify the potential object of interest class further into classes to detect the object of interest class. In the conducted experiments using real hyperspectral imagery, the results of the proposed algorithm show comparatively low false alarm rate even with a challenging scenarios.

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M. Karakaya, M. S. Alam, and M. I. Elbakary "Target detection in high-dimensional space using a stochastic expectation maximization algorithm", Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 656509 (7 May 2007); https://doi.org/10.1117/12.718394

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