A Markovian scheme for joint retrieval of classification and height map from urban interferometric SAR images

Synthetic aperture radar (SAR) interferometry enables to compute an height map of the scene which is useful for many applications. Yet the complexity of high resolution SAR images and of urban areas prevents from computing an accurate DSM easily and a high level processing chain is thus required. In this article, we propose a Markovian fusion scheme to retrieve jointly the height map and the classification. The original data (amplitude, interferogram and coherence) are first processed in order to get new entries. They represent advanced information on the scene, extracted with different approaches (filtering, object recognition or global classification). These features are then merged in a Markovian framework to recover an improved classification and height map. The method is illustrated on real data.