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DOE OSTI · 1396159

Data Analytics for SAR

Murphy, David Patrick·Calef, Matthew Thomas
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Abstract

We assess the ability of variants of anomalous change detection (ACD) to identify human activity associated with large outdoor music festivals as they are seen from synthetic aperture radar (SAR) imagery collected by the Sentinel-1 satellite constellation. We found that, with appropriate feature vectors, ACD using random-forest machine learning was most effective at identifying changes associated with the human activity.

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BibTeXRIS

Murphy, David Patrick, Calef, Matthew Thomas. 2017-10-02. Data Analytics for SAR. https://doi.org/10.2172/1396159

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