Share Email Print
cover

Proceedings Paper

Gray-scale moment invariants for airborne mine detection, discrimination and false alarm mitigation
Author(s): Pradeep Sriram; Sanjeev Agarwal; O. Robert Mitchell
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Shape features based on gray-scale moment invariants are presented for airborne mine detection and discrimination. Eleven shape features are obtained by translation, rotation and contrast normalization of the fourth-order gray-scale moments. Mahalanobis distance between an observed and true (average) shape feature vector is used as a shape metric. Covariance matrix corresponding to the average shape feature vector is obtained analytically using an additive and multiplicative noise model for the MWIR image. Effectiveness of gray scale moment invariant shape features for mine discrimination and false alarm mitigation is shown using MWIR imagery collected for LAMD-I program in May 2000. Successful implementation of the features in an airborne detection depends on the consistency of these shape features over time with change in factors such as solar illumination, ageing, clouds and environmental conditions. A study of the variability of gray-scale moment invariant-based shape features with time is conducted using MWIR time-sequenced imagery acquired in June-July 1998 by E-OIR.

Paper Details

Date Published: 13 August 2002
PDF: 12 pages
Proc. SPIE 4742, Detection and Remediation Technologies for Mines and Minelike Targets VII, (13 August 2002); doi: 10.1117/12.479070
Show Author Affiliations
Pradeep Sriram, Univ. of Missouri/Rolla (United States)
Sanjeev Agarwal, Univ. of Missouri/Rolla (United States)
O. Robert Mitchell, Univ. of Missouri/Rolla (United States)


Published in SPIE Proceedings Vol. 4742:
Detection and Remediation Technologies for Mines and Minelike Targets VII
J. Thomas Broach; Russell S Harmon; Gerald J. Dobeck, Editor(s)

© SPIE. Terms of Use
Back to Top