Efficient acquisition and utilization of remotely sensed data requires an extensive a priori evaluation of the performance of the basic data collection unit, the multispectral scanner. The objective is the development of a fully parametric technique to theoretically evaluate the systems response in any desired operational environment and provide the necessary information in selecting a set of optimum parameters.
In this paper the multispectral scanner spatial characteristics are represented by a linear shift-invariant multiple-port system where the N spectral bands comprise the input processes. The scanner characteristic function, the relationship governing the transformation of the input spatial and hence spectral correlation matrices through the systems, is developed. Specific cases for Gaussian point spread functions are examined.
The integration of the scanner spatial model and a parameter classification error estimator provides the necessary technique to evaluate the performance of a multispectral scanner. A set of test statistics are specified and the corresponding output quantities computed by the characteristic function. Two sets of classification accuracies, one at the input and one at the output are estimated. The scanner's instantaneous field of view is changed and the variation of the output classification performance monitored.
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