Correlation ATR performance using Xpatch (synthetic) training data

Abhijit Mahalanobis; Luis A. Ortiz; Bhagavatula Vijaya Kumar; Albert Ezekiel

doi:10.1117/12.396345

24 August 2000 Correlation ATR performance using Xpatch (synthetic) training data

Abhijit Mahalanobis, Luis A. Ortiz, Bhagavatula Vijaya Kumar, Albert Ezekiel

Proceedings Volume 4053, Algorithms for Synthetic Aperture Radar Imagery VII; (2000) https://doi.org/10.1117/12.396345
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

In this paper, we discuss the performance of correlation filter algorithms trained on Xpatch (synthetic) model images. In particular, we assess the performance of the maximum average correlation height (MACH) filter and distance classifier correlation filter (DCCF) correlation algorithms on a 3-class subset of the public release MSTAR data set. The successful performance of these algorithms on a 10-class problem has been reported in previous publications. The results reported to date however were based on filters trained on actual sensor data. The approach proposed here is viewed as a means to combine advantages of purely model-based techniques and the statistical/correlation based approaches. The paper reviews the theory of the algorithm, key practical advantages and details of test results on the 3-class public MSTAR database.

Citation Download Citation

Abhijit Mahalanobis, Luis A. Ortiz, Bhagavatula Vijaya Kumar, and Albert Ezekiel "Correlation ATR performance using Xpatch (synthetic) training data", Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); https://doi.org/10.1117/12.396345

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