Small object classification performance of high-resolution imaging sonars as a function of image resolution

Several results from an investigation of the relationship between the classification performance of high-resolution imaging sonars and image resolution and image signal-to-noise ratio (SNR) are presented. The primary goal of this investigation has been to develop a capability to accurately estimate the classification performance of various high resolution imaging sonars used for minehunting. An additional goal has been to develop a baseline measure of classification capability that can enable more accurate evaluations of the relative performance capabilities of developmental CAD/CAC algorithms. The investigation is being conducted using synthetic sonar images, due to a severe lack of real ground-truthed image data sets with directly comparable object types, multiple resolutions, and multiple calibrated SNR values, Image data sets containing an equal number of synthetic images with equal range and cross-range resolutions of 1, 3, 6, and 9-inches were created. The individual image data sets for these four resolutions include the same mine and minelike objects at the same ranges and orientations to facilitate a direct performance comparison. The image backgrounds are Rayleigh distributed and the image SNR values range from 3 to 15 dB. The classification performance results were obtained using one of the advanced computer-aided detection and classification (CAC) algorithms that are currently in the process of transitioning to several U.S. Navy minehunting systems. Standard Receiver Operating Characteristic (ROC) curves for the joint probability of detection and classification and probability of false alarm as a function of "effective SNR" are presented.