Mine detection in multispectral imagery data using constrained energy minimization

M. I. Elbakary; M. S. Alam

doi:10.1117/12.781735

17 March 2008 Mine detection in multispectral imagery data using constrained energy minimization

M. I. Elbakary, M. S. Alam

Proceedings Volume 6977, Optical Pattern Recognition XIX; 697707 (2008) https://doi.org/10.1117/12.781735
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

Multispectral imagery is used for a wide variety of military and commercial applications, including object detection such as mines. The main reason for using multispectral imagery is that it reveals spectral information about the scene which cannot be obtained from a single spectral band. This paper introduces a new algorithm for mine detection in multispecral imagery using the constrained energy minimization (CEM) approach. The CEM approach is introduced as classifier. The novelty of this idea is that this classifier uses only the information of the mines for training and enabling the potential mines without using information about the clutter in the scene. Using only mines information for detection is a major advantage of the CEM approach. In addition, the CEM approach is modified such that recomputing the autocorrelation matrix is not necessary and using the algorithm became scene independent Then, to reduce the false alarm further, morphological processing and stochastic expectation maximization (SEM) algorithm are employed for post-processing. The results of the proposed algorithm were promising when the algorithm is tested using real multispectral imagery.

Citation Download Citation

M. I. Elbakary and M. S. Alam "Mine detection in multispectral imagery data using constrained energy minimization", Proc. SPIE 6977, Optical Pattern Recognition XIX, 697707 (17 March 2008); https://doi.org/10.1117/12.781735

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