NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
Application of AI techniques to infer vegetation characteristics from directional reflectance(s)Traditionally, the remote sensing community has relied totally on spectral knowledge to extract vegetation characteristics. However, there are other knowledge bases (KB's) that can be used to significantly improve the accuracy and robustness of inference techniques. Using AI (artificial intelligence) techniques a KB system (VEG) was developed that integrates input spectral measurements with diverse KB's. These KB's consist of data sets of directional reflectance measurements, knowledge from literature, and knowledge from experts which are combined into an intelligent and efficient system for making vegetation inferences. VEG accepts spectral data of an unknown target as input, determines the best techniques for inferring the desired vegetation characteristic(s), applies the techniques to the target data, and provides a rigorous estimate of the accuracy of the inference. VEG was developed to: infer spectral hemispherical reflectance from any combination of nadir and/or off-nadir view angles; infer percent ground cover from any combination of nadir and/or off-nadir view angles; infer unknown view angle(s) from known view angle(s) (known as view angle extension); and discriminate between user defined vegetation classes using spectral and directional reflectance relationships developed from an automated learning algorithm. The errors for these techniques were generally very good ranging between 2 to 15% (proportional root mean square). The system is designed to aid scientists in developing, testing, and applying new inference techniques using directional reflectance data.
Document ID
19950010639
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Kimes, D. S.
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Smith, J. A.
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Harrison, P. A.
(JJM Systems, Inc. Arlington, VA., United States)
Harrison, P. R.
(Naval Academy Annapolis, MD., United States)
Date Acquired
August 16, 2013
Publication Date
January 1, 1994
Publication Information
Publication: CNES, Proceedings of 6th International Symposium on Physical Measurements and Signatures in Remote Sensing
Subject Category
Earth Resources And Remote Sensing
Accession Number
95N17054
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
Document Inquiry

Available Downloads

There are no available downloads for this record.
No Preview Available