Explorations in transfer learning and machine learning architectures utilizing the DSIAC ATR algorithm development data set

Kevin L. Priddy; Sastry Dhara

doi:10.1117/12.2662873

13 June 2023 Explorations in transfer learning and machine learning architectures utilizing the DSIAC ATR algorithm development data set

Kevin L. Priddy, Sastry Dhara

Author Affiliations +

Proceedings Volume 12521, Automatic Target Recognition XXXIII; 125210K (2023) https://doi.org/10.1117/12.2662873
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

This paper presents the results of applying several transfer learning front-ends (e.g. Resnet50, Inception, MobileNet) commonly utilized in academia based upon the ImageNet database to perform feature extraction for the DSIAC ATR data set followed by classification layers. This paper describes the performance of a machine learning system (MLS) composed of a feature generating front-end followed by a classification backend trained on electro-optical (EO) and mid-wave infrared (MWIR) imagery from the DSIAC dataset. The baseline MLS architecture achieves over 99 percent accuracy on both the EO and MWIR DSIAC datasets.

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Kevin L. Priddy and Sastry Dhara "Explorations in transfer learning and machine learning architectures utilizing the DSIAC ATR algorithm development data set", Proc. SPIE 12521, Automatic Target Recognition XXXIII, 125210K (13 June 2023); https://doi.org/10.1117/12.2662873

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