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Here's an up-to-date, comprehensive review of surveillance and reconnaissance (S&R) imaging system modeling and performance prediction. This new, one-of-a-kind resource helps you predict the information potential of new surveillance system designs, compare and select from alternative measures of information extraction, relate the performance of tactical acquisition sensors and surveillance sensors, and understand the relative importance of each element of the image chain on S&R system performance. It provides you with system descriptions and characteristics, S&R modeling history, and performance modeling details. With an emphasis on validated prediction of human observer performance, this unique book addresses the specific design and analysis techniques used with today's S&R imaging systems. You find in-depth discussions on everything from the conceptual performance prediction model, linear shift invariant systems, and measurement variables used for S&R information extraction - to predictor variables, target and environmental considerations, CRT and flat panel display selection, and models for image processing. Conversion methods between alternative modeling approaches are examined to help you perform system comparisons. Supported with 120 illustrations and 150 equations.
Table Of Contents
Introduction - Modeling Applications. Conceptual S&R Model. Performance Prediction Approaches.; Surveillance and Reconnaissance Systems -Introduction. Imaging with IR and EO Sensors. Image Formation. Imaging System parameters. Synthetic Aperture Radar. Choice of Imaging Sensors. Examples of Surveillance and Reconnaissance Sensors.; Historical Review of S&R Modeling - Pre-1950. 1950-1970. 1970-Present.; Linear Shift Invariant Imaging Systems - Linearity and Shift Invariance. The Impulse Function. The Fourier Series and Fourier Transform. LSI Imaging System. Imaging in the Space and Frequency Domain. Imaging With Components. Simplifying LSI Imager Analysis to One Dimension. Sampled Imaging Systems. SAR Impulse Response and Transfer Function.; Information Extraction Measures - Direct Performance Measures. Theory of Signal Detection. Range/Time Measures. Performance Estimate Measures. Information and Difference Metrics.; Information Extraction Performance Predictors - Scale, Resolution, and Sharpness. Contrast and Noise. Artifacts. Summary Measures.; Target and Environmental Considerations - Target Effects. Deception and Denial. Atmospheric Effects.; Image Processing Considerations - Bandwidth Compression. Enhancement Processing.; Display and Observer Considerations - Displays. Observer Characteristics. Observer Models.; Performance Prediction Models - Parameter-based Models. Image-Based Models.; Sensor Performance Conversions - Johnson Criteria and NIIRS. Conversion with Models. Comparing Sensor Performance Conversions. Performance Conversion as a Function of Target Size.; Conclusions and Future Directions - Spectral Domain. Temporal Effects. Search. SAR.;
Ronald G. Driggers
Ronald Driggers is the superintendent in the Optical Sciences Division of the U.S. Naval Research Laboratory. He was previously a senior engineer at U.S. Army Night Vision and Electronic Sensors Directorate where he provided electro-optical and infrared research on performance modeling. Dr. Driggers received his Ph.D., M.S., and B.S. from the University of Memphis.
Jon C. Leachtenauer
Jon C. Leachtenauer formed his own consulting agency, J/M Leachtenauer Associates, Inc, and is currently a consultant to the National Imagery and Mapping Agency. He is the author of over 150 technical reports, as well as numerous published papers covering all aspects of the image exploitation process. Mr. Leachtenauer holds an A.B. and M.S. in Geology from Syracuse University.