Relevance feedback for satellite image change detection

Due to the exponential growth of satellite image collections, there is an increasing need for automatic solutions that assist operators in different applications. Automatic change detection is one of these applications that received an increasing attention during the last years. Nevertheless, fully automatic solutions reach their limitation; on the one hand, it is difficult to build general decision criteria able to select area of changes for different images, and on the other hand, the relevance of changes may differ from one user to another. In this paper, we introduce an alternative change detection method based on relevance feedback. The proposed algorithm is iterative and based on a query and answer (Q&A) model that (i) asks the user the most informative questions about the relevance of his targeted changes, and (ii) according to these answers updates change detection results. Experiments conducted on large satellite images, show that indeed the approach is effective and allows the user to retrieve almost all his targeted changes while discarding the untargeted ones, with a negligible interaction effort.