Semi-Supervised and Unsupervised Novelty Detection using Nested Support Vector Machines

Very often in change detection only few labels or even none are available. In order to perform change detection in these extreme scenarios, they can be considered as novelty detection problems, semi-supervised (SSND) if some labels are available otherwise unsupervised (UND). SSND can be seen as an unbalanced classification between labeled and unlabeled samples using the Cost-Sensitive Support Vector Machine (CS-SVM). UND assumes novelties in low density regions and can be approached using the One-Class SVM (OC-SVM). We propose here to use nested entire solution path algorithms for the OC-SVM and CS-SVM in order to accelerate the parameter selection and alleviate the dependency to labeled ``changed'' samples. Experiments are performed on two multitemporal change detection datasets (flood and fire detection) and the performance of the two methods proposed compared.


Published in:
Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium
Presented at:
IEEE International Geoscience and Remote Sensing Symposium, Munich, germany, July 22-27, 2012
Year:
2012
Publisher:
New York, Ieee
ISBN:
978-1-4673-1159-5
Keywords:
Laboratories:




 Record created 2012-03-28, last modified 2018-01-28

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