The Hotelling trace criterion (HTC) is used to find a set of linear features that optimally separate two classes of objects. The objects used in our study were simulated livers with and without tumors, with noise, blur, and object variability. Using the receiver-operating-characteristic parameter da as our measure, we have found that the ability of the HTC to separate these objects into their correct classes, by detecting the presence or absence of a tumor, has a correlation of 0.988 with the ability of humans to separate the same two classes of objects. This suggests, therefore, that the HTC can be used as a figure of merit for optimizing system parameters, since it calculates a single, scalar figure of merit that has a high correlation with human-observer performance.

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Commercial remote sensing systems generally employ a linear detector array for imaging the ground scene. Strategies for increasing the ground sampling along one direction of the array can be employed to improve the image quality. Image simulations were generated to quantify the image quality improvement, in terms of the National Image Interpretability Scale (NIIRS), as the sampling is increased in the along-scan (A/S) direction of the array. The simulations modeled a remote sensing system with FN/pϭ1 (where FN is the system f-number and p is the detector sampling pitch) and show that an image quality improvement of approximately 0.35 NIIRS can be achieved if the sampling rate is increased in the A/S direction by 2ϫ. © 1999 Society of PhotoOptical Instrumentation Engineers. [S0091-3286(99)