Detecting changes between two satellite images of the same scene generally requires an accurate (and thus often uneasy to obtain) model discriminating relevant changes from irrelevant ones. We here present a generic method, based on the definition of four different a-contrario detection models (associated to arbitrary features), whose aggregation is then trained from specific examples with gradient boosting. The results we present are encouraging, and in particular the low false positive rate is noticeable.