Performance of the MACH filter and DCCF algorithms on the 10-class public release MSTAR data set

Abhijit Mahalanobis; Luis A. Ortiz; Bhagavatula Vijaya Kumar

doi:10.1117/12.357646

13 August 1999 Performance of the MACH filter and DCCF algorithms on the 10-class public release MSTAR data set

Abhijit Mahalanobis, Luis A. Ortiz, Bhagavatula Vijaya Kumar

Proceedings Volume 3721, Algorithms for Synthetic Aperture Radar Imagery VI; (1999) https://doi.org/10.1117/12.357646
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

The maximum average correlation height (MACH) filter and distance classifier correlation filter (DCCF) correlation algorithms are evaluated using the 10 class publicly released MSTAR database. The successful performance of these algorithms on a 3-class problem has been previously reported. The algorithms are optimized by design to be robust to variations (distortions) in the target's signature as well as discriminate between classes. Unlike Matched Filtering (or other template based methods), the proposed approach requires relatively few filters. The paper reviews the theory of the algorithm, key practical advantages and details of test results on the 10-class public MSTAR database.

Citation Download Citation

Abhijit Mahalanobis, Luis A. Ortiz, and Bhagavatula Vijaya Kumar "Performance of the MACH filter and DCCF algorithms on the 10-class public release MSTAR data set", Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); https://doi.org/10.1117/12.357646

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