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Performance of automatic target recognition from synthetic aperture sonardata is heavily dependent on the complexity of the beamformed imagery. Severalmechanisms can contribute to this, including unwanted vehicle dynamics, thebathymetry of the scene, and the presence of natural and manmade clutter. Tounderstand the impact of the environmental complexity on image perception,researchers have taken approaches rooted in information theory, or heuristics.Despite these efforts, a quantitative measure for complexity has not beenrelated to the phenomenology from which it is derived.  By using subject matter experts (SMEs) we derive a complexity metric for aset of imagery which accounts for the underlying phenomenology. The goal ofthis work is to develop an understanding of how several common informationtheoretic and heuristic measures are related to the SME perceived complexity insynthetic aperture sonar imagery. To achieve this, an ensemble of 10-meter x10-meter images were cropped from a high-frequency SAS data set that spansmultiple environments. The SME's were presented pairs of images from which theycould rate the relative image complexity. These comparisons were then convertedinto the desired sequential ranking using a method first developed by A. Elofor establishing rankings of chess players.  The Elo method produced a plausible rank ordering across the broad dataset.The heuristic and information theoretical metrics were then compared to theimage rank from which they were derived. The metrics with the highest degree ofcorrelation were those relating to spatial information, e.g. variations inpixel intensity, with an R-squared value of approximately 0.9. However, thisagreement was dependent on the scale from which the spatial variation wasmeasured. Results will also be presented for many other measures includinglacunarity, image compression, and entropy.

Measuring Human Assessed Complexity in Synthetic Aperture Sonar Imagery Using the Elo Rating System