Multitemporal analysis of multisensor data: Information theoretical approaches

This paper presents two approaches for the analysis of multi-temporal and multisensory data analysis. The first approach focuses on a novel similarity measure, named Mixed Information, derived from information measures and assessment of its performance for multitemporal analysis in change detection. The second approach proposes the use of Kullback-Leibler divergence between marginal distributions, efficiently approximated by cumulant based expansion series. Comparison between mixed information and Kullback-Leibler divergence based change detection is performed to confirm the usefulness of joint metric for analyzing multitemproal and multisensory data. Experimental results obtained confirm justification of the mixed information in multitemporal analysis.