Quantifying Image Quality Effects On Automatic Target Detection In Sar Imagery

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 inter...

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Bibliographic Details
Main Authors: Taylor, Jared, Brown, Paul, Irvine, John M.
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
deep learning
image chain analysis
image quality
Machine learning
Object detection
Pattern recognition
Radar imaging
Radar polarimetry
Synthetic aperture radar
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Summary: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 than 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 versus human perception.
ISSN:2332-5615
DOI:10.1109/AIPR60534.2023.10440691