An exploration of NIIRS, image quality, and machine learning

John M. Irvine; Steven A. Israel

doi:10.1117/12.2560587

21 April 2020 An exploration of NIIRS, image quality, and machine learning

John M. Irvine, Steven A. Israel

Proceedings Volume 11398, Geospatial Informatics X; 113980J (2020) https://doi.org/10.1117/12.2560587
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

The interpretability of an image indicates the potential intelligence value of the data. Historically, the National Imagery Interpretability Rating Scale (NIIRS) has been the standard for quantifying the intelligence potential based on image analysis by human observers. Empirical studies have demonstrated that spatial resolution is the dominant predictor of the NIIRS level of an image. Today, the value of imagery is no longer simply determined by spatial resolution, since additional factors such as spectral diversity and temporal sampling are significant. Furthermore, analyses are performed by machines as well as humans. Consequently, NIIRS no longer accurately quantifies potential intelligence value for an image or set of images. We are exploring new measures of information potential based on mutual information. Our research suggests that new measures of image “quality” based on information theory can provide meaningful standards that go beyond NIIRS. In our approach, mutual information provides an objective method for quantifying divergence across objects and activities in an image. This paper presents the rationale for our approach, the technical description, and the results of early experimentation to explore the feasibility of establishing an information-theoretic standard for quantifying the intelligence potential of an image.

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John M. Irvine and Steven A. Israel "An exploration of NIIRS, image quality, and machine learning", Proc. SPIE 11398, Geospatial Informatics X, 113980J (21 April 2020); https://doi.org/10.1117/12.2560587

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