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This paper introduces a spatial pattern recognition processing concept involving the use of spectral feature classification technology and coherent optical correlation. The concept defines a hybrid image (data) processing system incorporating both digital and optical technology. In this concept, the hybrid instrument provides simplified pseudopattern images as functions of pixel classification from information embedded within a real-scene image. These pseudoimages become simplified inputs to an optical correlator for use in a subsequent pattern identification decision useful in executing landmark pointing, tracking, or navigating functions. In this concept, real-time classification is proposed as a research tool for exploring ways to enhance input signal-to-noise ratio as an aid in improving optical correlation. Although such a system has not been developed or tested, the approach could be explored with developing technology, including a current NASA Langley Research Center technology plan that involves a series of related Shuttle-borne experiments. A first-planned development phase includes a feature classification experiment. The experiment, Feature Identification and Location Experiment (FILE), is undergoing final ground testing, and is scheduled for flight on the NASA Shuttle (STS2/flight OSTA-1) in 1980. FILE will evaluate a technique for autonomously (in-real-time) classifying Earth features into the four categories of bare land; water; vegetation; and clouds, snow, or ice. A second experiment is being designed to test a technique for autonomously discriminating between clouds and snow. Beyond feature identification/classification and cloud detection/discrimination, the onnoing technology development leads to capabilities for pointing instruments to predetermined sites, reacquiring Earth features or landmarks, and tracking features such as coastlines or rivers. Concepts are discussed relative to emerging technology,and directions for future research are indicated.
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