Review of recent advances in AI/ML using the MSTAR data

Erik Blasch; Uttam Majumder; Edmund Zelnio; Vincent Velten

doi:10.1117/12.2559035

19 May 2020 Review of recent advances in AI/ML using the MSTAR data

Erik Blasch, Uttam Majumder, Edmund Zelnio, Vincent Velten

Proceedings Volume 11393, Algorithms for Synthetic Aperture Radar Imagery XXVII; 113930C (2020) https://doi.org/10.1117/12.2559035
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Over the past decades, there have been many approaches to synthetic aperture radar (SAR) automatic target recognition (ATR). ATR includes detection, classification, and identification of targets, scene, and context. Recently, the explosion of methods for deep learning has attracted numerous researchers to compare machine learning methods for SAR ATR. This paper reviews many approaches conducted for SAR recognition and discerns the most promising approaches. Using the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set, there are comparative methods to evaluate the advances from the community. The paper reviews many of the available techniques recently published to determine the state of the art in emerging concepts.

Conference Presentation

Citation Download Citation

Erik Blasch, Uttam Majumder, Edmund Zelnio, and Vincent Velten "Review of recent advances in AI/ML using the MSTAR data", Proc. SPIE 11393, Algorithms for Synthetic Aperture Radar Imagery XXVII, 113930C (19 May 2020); https://doi.org/10.1117/12.2559035

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