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

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

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

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available