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|Title:||Information theoretical similarity measure for change detection|
|Authors:||GUEGUEN LIONEL; CUI Shiyong; DATCU Mihai|
|Citation:||Proceedings of the 2011 Joint Urban Remote Sensing Event JURSE 2011 - ISBN 978-1-4244-8657-1 p. 69-72|
|Publisher:||Institute of Electrical and Electronics Engineers Inc. (IEEE)|
|Type:||Contributions to Conferences|
|Abstract:||In this paper, mixed information similarity measure and a multidimensional density estimation method based on multivariate Edgeworth series expansion are proposed and assessed for the task of multi-temporal change detection. To unify mutual information and variational information, mixed information is proposed to quantify the degree of dependence between two random variables, which are intuitively appropriate for multi-temporal change detection. In the literature, Edgeworth series expansion is widely used in statistics and various engineering fields for one-dimensional density estimation. To compute the mixed information measure, multidimensional density estimation based on multivariate Edgeworth series expansion is proposed and evaluated. Two experiments on real SAR images and optical images are carried out to evaluate the performance of change detection. Experimental results confirm the promising capability of mixed information and the multivariate density estimation based on Edgeworth series expansion.|
|JRC Institute:||Institute for the Protection and Security of the Citizen|
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