Paper Info
Title
An Application of Possibility Theory Information Fusion to Satellite Image Classification
Abstract
This paper presents the application of an adaptive information fusion operator developed in the framework of possibility theory for the supervised classification of multisource remote sensing images. This operator is low CPU time consuming and carries out classification rates comparable to maximum likelihood. The adaptive operator has been designed to merge several agreeing information sources which might be in conflict from time to time. It has been used for the classification of two data sets: a Landsat MSS image and GIS data on the one hand, and multitemporal SPOT XS visible images on the other hand. Satellite images of the same scene are redundant and complementary, and this operator is efficient at handling this kind of data.
Year
DOI
Venue
1997
10.1007/BFb0095077
Fuzzy Logic in Artificial Intelligence (IJCAI Workshop)
Keywords
Field
DocType
possibility theory information fusion,satellite image,possibility theory,maximum likelihood
Information processing,Pattern recognition,Computer science,CPU time,Multispectral image,Image processing,Possibility theory,Sensor fusion,Artificial intelligence,Operator (computer programming),Contextual image classification
Conference
ISBN
Citations 
PageRank 
3-540-66374-6
2
0.42
References 
Authors
4
1
Name
Order
Citations
PageRank
Ludovic Roux1517.25