Vitenskapelig Kapittel/Artikkel/Konferanseartikkel   2011

Salberg, Arnt-Børre

Publikasjonsdetaljer

Sider:

166 –169

År:

2011

Lenker:

OMTALE: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6048958
FULLTEKST: http://dx.doi.org/10.1109/IGARSS.2011.6048958
DOI: doi.org/10.1109/igarss.2011.6048958

Del av: Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International (IEEE Press, 2011)

In this paper we propose a method for retraining a maximum likelihood classifier such that it may be applied to cases when the data distribution of the test data is different from the training data distributions. The proposed approach for retraining the classifier to the test data distribution is based on a constrained low-rank modeling of the unknown parameters, and may be designed such that the class structure is (to a larger degree) maintained after retraining. The proposed methodology is evaluated on two different applications; (1) cloud detection in Quickbird and WorldView-2 images and (2) tree cover mapping of tropical forest. The results show that the retrained classifiers clearly outperform their non-retrained counterpart.