Cascade multitemporal classification based on fuzzy Markov chains

This paper proposes a new fuzzy cascade multitemporal classification method based on Fuzzy Markov Chains. This method differs from prior fuzzy multitemporal approaches proposed thus far, as the method does not require the knowledge of the true class at an earlier date; instead it uses the attributes of the image object being classified at the earlier date. This method combines the fuzzy, non-temporal, classification of a geographical region at two points in time to provide a single unified result. A transformation law based on class transition possibilities projects the earlier classification onto the later date before combining both results. Performance analysis was conducted upon a sequence of three LANDSAT images from the central region of Brazil using a Genetic Algorithm to estimate transition possibilities. The analysis showed that the increase in performance is highly dependent on whether or not a significant correlation exists between the temporal data sets, as well as on the accuracy of the monotemporal classifier at the earlier date. While the monotemporal approach used in the experiments attained an average class accuracy of approximately 55%, the multitemporal scheme achieved between 65% and 95%. Similar results in terms of overall accuracy were also observed. Furthermore, compared to two alternative cascade multitemporal classification approaches, the proposed method consistently showed better results.