基于知识的植被特征推断系统(veg)的新进展

D.S. Kirnes, P. Harrison
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引用次数: 1

摘要

已经开发了一种利用最低点和/或定向反射数据作为输入推断植被物理和生物表面特性的提取技术。基于知识的系统(VEG)接受未知目标的光谱数据作为输入,确定推断所需植被特征的最佳策略,将该策略应用于目标数据,并提供严格的推断精度估计。介绍了系统的开发进展。VEG结合了遥感和人工智能的方法,并将输入光谱测量与多种知识库相结合。VEG已经发展到(1)从最低点和/或非最低点视角的任何组合推断光谱半球反射率;(2)在内部光谱数据库上测试和开发新的提取技术;(3)浏览、绘制或分析系统光谱数据库中的定向反射数据;(4)利用光谱和方向反射关系区分用户定义的植被类别;(5)从已知视角推断未知视角(称为视角扩展)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New Developments Of A Knowledge Based System (veg) For Inferring Vegetation Characteristics
An 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).
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