UNSUPERVISED CHANGE DETECTION FOR MULTISPECTRAL IMAGES


G Jeyaram, V Vidhya, K R Ishwarya

Abstract


This paper presents a novel approach to unsupervised change detection in multispectral remote-sensing images. The proposed approach aims at extracting the change information by jointly analyzing the spectral channels of multitemporal images without any training data. This is accomplished by using a selective Bayesian thresholding for deriving a pseudo training set that is necessary for initializing an adequately defined binary semisupervised support vector machine (S3VM) classifier. Starting from these initial seeds, the S3VM performs change detection in the original multitemporal feature space by gradually considering unlabeled patterns in the definition of the decision boundary between changed and unchanged pixels according to a semisupervised learning algorithm. The values of the classifier parameters are then defined according to a novel unsupervised model-selection technique based on a similarity measure between change-detection maps obtained with different settings.


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References


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About this article

Title

UNSUPERVISED CHANGE DETECTION FOR MULTISPECTRAL IMAGES

Date

01-04-2013

Additional Links

Manuscript Submission

Journal

Innovare Journal of Engineering & Technology
Vol 1 Issue 1 2013 (April-June) Page: 8-11

Online ISSN

2347-1573

Statistics

127 Views | 131 Downloads

Authors & Affiliations

G Jeyaram
India

V Vidhya
India

K R Ishwarya
India


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