Fuzzy based change detection in multitemporal fraction images

In this paper, a new concept to change detection in remote sensing multitemporal images is presented. Traditional methods are generally concerned to label pixels into two exhaustive classes: change or no change. Even this approach is more common used, real environmental changes tend to occur in a continuum, rather than sudden manner. The proposed methodology is based on Bayesian framework and fraction images in order to classify pixels according to degrees of membership to the class change, in a fuzzy-like fashion. An experiment is performed employing synthetic image simulating realistic changes. The result shows that the methodology can adequately tell about the gradual changes occurred between two dates.