Paper Info
Title
Fusion of multi-temporal high resolution optical image series and crop rotation information for land-cover map production.
Abstract
The generation of land-cover maps for agriculture is a recurrent problem in remote sensing. There exist many efficient algorithms, but they often need well selected images during specific periods, which delays the map availability to the end of the season. In this work, we propose to introduce prior knowledge about the crop rotation in order to both improve the classification and obtain an accurate map early in the year. We use a Bayesian Network to model the crop rotation and we introduce the output of the model into a Support Vector Machine classifier to generate a land-cover map. We evaluate the overall improvement and the effect on several crops.
Year
DOI
Venue
2012
10.1109/IGARSS.2012.6352606
IGARSS
Keywords
Field
DocType
Bayes methods,agriculture,crops,geophysical image processing,image classification,image resolution,optical images,terrain mapping,vegetation mapping,Bayesian network,agriculture,crop rotation information,image classification,land-cover map production,multitemporal high resolution optical image fusion series,remote sensing,support vector machine,Bayesian networks,Land cover maps,Support Vector Machines,crop rotations,time series
Computer vision,Optical image,Support vector machine classifier,Computer science,Remote sensing,Support vector machine,Bayesian network,Artificial intelligence,Contextual image classification,Crop rotation,Image resolution,Land cover
Conference
ISSN
Citations 
PageRank 
2153-6996
1
0.38
References 
Authors
0
5
Name
Order
Citations
PageRank
Julien Osman131.10
Jordi Inglada242643.13
Jean-Francois Dejoux375.45
Olivier Hagolle417724.29
Gérard Dedieu514930.83