Quantifying image quality effects on automatic target detection in SAR imagery

Jared Taylor; Paul A. Brown; John M. Irvine

doi:10.1117/12.2664121

15 June 2023 Quantifying image quality effects on automatic target detection in SAR imagery

Jared Taylor, Paul A. Brown, John M. Irvine

Proceedings Volume 12521, Automatic Target Recognition XXXIII; 125210E (2023) https://doi.org/10.1117/12.2664121
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Automatic Target Detection (ATD) leverages machine learning to efficiently process datasets that are too large for humans to evaluate quickly enough for practical applications. Technological and natural factors such as the type of sensor, collection conditions, and environment can affect image interpretability. Synthetic Aperture Radar (SAR) sensors are sensitive to different issues from optical sensors. While SAR imagery can be collected at any time of day and in almost any weather conditions, some conditions are uniquely challenging. Properties of targets and the environment can affect the radar signatures. In this experiment, we simulated these effects in quantifiable increments to measure how strongly they impact the performance of a machine learning model when detecting targets. The experiments demonstrate the differences in image interpretability for machine learning vs. human perception.

Conference Presentation

Citation Download Citation

Jared Taylor, Paul A. Brown, and John M. Irvine "Quantifying image quality effects on automatic target detection in SAR imagery", Proc. SPIE 12521, Automatic Target Recognition XXXIII, 125210E (15 June 2023); https://doi.org/10.1117/12.2664121

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