Abstract

An extension of a new technique that enables parallel, simultaneous processing of remotely obtained images is presented. The technique holds out promise for automatic on-board classification of data. The central feature involves the generation of binary masks, directly from the image, based on object reflectance data, that groups objects into equivalence classes. These masks, called equivalence class masks, can be used in various logical combinations to isolate classes of objects with a priori known reflectance or radiance signatures. Experimental verification of the technique is furnished for simple scenes. A computational scheme, based on a sequence of integrated irradiance measurements on the image, that enables the identification of objects within an equivalence class is suggested.

© 1972 Optical Society of America

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