Abstract
Traditional performance metrics for hyperspectral imaging (HSI) systems include signal-to-noise ratio (SNR), ground sample distance (GSD), ground resolvable distance (GRD), and noise equivalent spectral radiance (NESR). These metrics characterize the sensor system itself, but there is a gap between these metrics and the ability of the system to detect and identify targets in realistic scenes. Three additional metrics, the target size to GRD ratio, target spectral variability and receiver operating characteristic (ROC) curve, are evaluated to quantify HSI system performance with scene and mission conditions considered. Historically, sensor design efforts do not use ROC curves because they are relatively difficult to calculate and depend on scene parameters as well as sensor parameters (e.g., SNR, GSD, GRD, and NESR). Data from a recent experiment in which an airborne sensor collected data for a variety of targets are used to identify exploit performance factors that need to be included into the model to quantify end-to-end sensor exploit performance. The primary targets consisted of an array of blue tarpaulins cut to sizes less than and greater than one HSI spatial pixel projected to the ground. We designed the experiment to quantify and compare the effects of target size on ROC curves. One key result of this work is that the radiance of targets in the scene exhibits a large degree of variation among many passes during two days of flight testing. This variability complicates the detection process. Another key result is that detection performance has a strong correlation with target size for subpixel targets. Finally, we demonstrate that in this case, sensor noise has little impact on detection performance.
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