DocumentCode
1444213
Title
Unsupervised retraining of a maximum likelihood classifier for the analysis of multitemporal remote sensing images
Author
Bruzzone, Lorenzo ; Prieto, Diego Fernàndez
Author_Institution
Dept. of Civil & Environ. Eng., Trento Univ., Italy
Volume
39
Issue
2
fYear
2001
fDate
2/1/2001 12:00:00 AM
Firstpage
456
Lastpage
460
Abstract
An unsupervised retraining technique for a maximum likelihood (ML) classifier is presented. The proposed technique allows the classifier´s parameters, obtained by supervised learning on a specific image, to be updated in a totally unsupervised way on the basis of the distribution of a new image to be classified. This enables the classifier to provide a high accuracy for the new image even when the corresponding training set is not available
Keywords
geophysical techniques; image classification; image sequences; maximum likelihood estimation; remote sensing; terrain mapping; geophysical measurement technique; image classification; image sequence; land surface; maximum likelihood classifier; multitemporal images; remote sensing; terrain mapping; unsupervised retraining; Availability; Image analysis; Image sensors; Maximum likelihood estimation; Pixel; Remote monitoring; Remote sensing; Sensor phenomena and characterization; Sensor systems; Supervised learning;
fLanguage
English
Journal_Title
Geoscience and Remote Sensing, IEEE Transactions on
Publisher
ieee
ISSN
0196-2892
Type
jour
DOI
10.1109/36.905255
Filename
905255
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