Nonparametric error estimation techniques applied to MSTAR data sets

Raman K. Mehra; Melvyn Huff; Ravi B. Ravichandran; Arnold C. Williams

doi:10.1117/12.321864

15 September 1998 Nonparametric error estimation techniques applied to MSTAR data sets

Raman K. Mehra, Melvyn Huff, Ravi B. Ravichandran, Arnold C. Williams

Proceedings Volume 3370, Algorithms for Synthetic Aperture Radar Imagery V; (1998) https://doi.org/10.1117/12.321864
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

The development of ATR performance characterization tools is very important for the design, evaluation and optimization of ATR systems. One possible approach for characterizing ATR performance is to develop measures of the degree of separability of the different target classes based on the available multi-dimensional image measurements. One such measure is the Bayes error which is the minimum probability of misclassification. Bayes error estimates have previously been obtained using Parzen window techniques on real aperture, high range resolution, radar data sets and on simulated synthetic aperture radar (SAR) images. This report extends these results to real MSTAR SAR data. Our results how that the Parzen window technique is a good method for estimating the Bayes error for such large dimensional data sets. However, in order to apply non-parametric error estimation techniques, feature reduction is needed. A discussion of the relationship between feature reduction and non-parametric estimation is included in this paper. The results of multimodal Parzen estimation on MSTAR images are also described. The tools used to produce the Bayes error estimates have been modified to produce Neyman-Pearson criterion estimates as well. Receiver Operating Characteristic curves are presented to illustrate non- parametric Neyman-Pearson error estimation on MSTAR images.

Citation Download Citation

Raman K. Mehra, Melvyn Huff, Ravi B. Ravichandran, and Arnold C. Williams "Nonparametric error estimation techniques applied to MSTAR data sets", Proc. SPIE 3370, Algorithms for Synthetic Aperture Radar Imagery V, (15 September 1998); https://doi.org/10.1117/12.321864

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