CONSENSUS THEORETIC CLASSIFICATION METHODS

被引：224

作者：

BENEDIKTSSON, JA ^{[1
]}

SWAIN, PH ^{[1
]}

机构：

[1] UNIV ICELAND,SIGNAL PROC & INFORMAT TECHNOL LAB,IS-107 REYKJAVIK,ICELAND

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS | 1992年 / 22卷 / 04期

基金：

美国国家航空航天局;

关键词：

D O I：

10.1109/21.156582

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Consensus theory is adopted as a means of classifying geographic data from multiple sources. The foundations and usefulness or different consensus theoretic methods are discussed in conjunction with pattern recognition. Weight selections for different data sources are considered and modeling of non-Gaussian data is investigated. The application of consensus theory in pattern recognition is tested on two data sets: 1) multisource remote sensing and geographic data and 2) very-high-dimensional remote sensing data. The results obtained using consensus theoretic methods are found to compare favorably with those obtained using well-known pattern recognition methods. The consensus theoretic methods can be applied in cases where the Gaussian maximum likelihood method cannot. Also, the consensus theoretic methods are computationally less demanding than the Gaussian maximum likelihood method and provide a means for weighting data sources differently.

引用

页码：688 / 704

页数：17

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