We discuss uncertainty quantification in multisensor data integration and analysis, including estimation methods and the role of uncertainty in decision making and trust in automated analytics. The challenges associated with automatically aggregating information across multiple images, identifying subtle contextual cues, and detecting small changes in noisy activity patterns are well-established in the intelligence, surveillance, and reconnaissance (ISR) community. In practice, such questions cannot be adequately addressed with discrete counting, hard classifications, or yes/no answers. For a variety of reasons ranging from data quality to modeling assumptions to inadequate definitions of what constitutes “interesting” activity, variability is inherent in the output of automated analytics, yet it is rarely reported. Consideration of these uncertainties can provide nuance to automated analyses and engender trust in their results. In this work, we assert the importance of uncertainty quantification for automated data analytics and outline a research agenda. We begin by defining uncertainty in the context of machine learning and statistical data analysis, identify its sources, and motivate the importance and impact of its quantification. We then illustrate these issues and discuss methods for data-driven uncertainty quantification in the context of a multi- source image analysis example. We conclude by identifying several specific research issues and by discussing the potential long-term implications of uncertainty quantification for data analytics, including sensor tasking and analyst trust in automated analytics.
David J. Stracuzzi, Michael C. Darling, Maximillian G. Chen, and Matthew G. Peterson, "Data-driven uncertainty quantification for multisensor analytics," Proc. SPIE 10635, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, 106350N (Presented at SPIE Defense + Security: April 17, 2018; Published: 4 May 2018); https://doi.org/10.1117/12.2304921.
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