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New developments of a knowledge based system (VEG) for inferring vegetation characteristicsAn extraction technique for inferring physical and biological surface properties of vegetation using nadir and/or directional reflectance data as input has been developed. A knowledge-based system (VEG) accepts spectral data of an unknown target as input, determines the best strategy for inferring the desired vegetation characteristic, applies the strategy to the target data, and provides a rigorous estimate of the accuracy of the inference. Progress in developing the system is presented. VEG combines methods from remote sensing and artificial intelligence, and integrates input spectral measurements with diverse knowledge bases. VEG has been developed to (1) infer spectral hemispherical reflectance from any combination of nadir and/or off-nadir view angles; (2) test and develop new extraction techniques on an internal spectral database; (3) browse, plot, or analyze directional reflectance data in the system's spectral database; (4) discriminate between user-defined vegetation classes using spectral and directional reflectance relationships; and (5) infer unknown view angles from known view angles (known as view angle extension).
Document ID
19930063758
Document Type
Conference Paper
Authors
Kimes, D. S. (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Harrison, P. A. (JJM Systems, Inc. Annapolis, MD, United States)
Harrison, P. R. (U.S. Naval Academy Annapolis, MD, United States)
Date Acquired
August 16, 2013
Publication Date
January 1, 1992
Publication Information
Publication: In: IGARSS '92; Proceedings of the 12th Annual International Geoscience and Remote Sensing Symposium, Houston, TX, May 26-29, 1992. Vol. 1 (A93-47551 20-43)
Subject Category
EARTH RESOURCES AND REMOTE SENSING
Distribution Limits
Public
Copyright
Other

Related Records

IDRelationTitle19930063554Analytic PrimaryIGARSS '92; Proceedings of the 12th Annual International Geoscience and Remote Sensing Symposium, Houston, TX, May 26-29, 1992. Vols. 1 & 2