Currently, tree maps are produced from field measurements that are time consuming and expensive. Application of existing techniques based on aerial photography is often hindered by cloud cover. This has initiated research into the segmentation of high resolution airborne interferometric Synthetic Aperture Radar (SAR) data for deriving tree maps. A robust algorithm is constructed to optimally position closed boundaries. The boundary of a tree crown will be best approximated when at all points on the boundary, the z-coordinate image gradient is maximum, and directed inwards orthogonal to the boundary. This property can be expressed as the result of a line integral along the boundary. Boundaries with a large value for the line integral are likely to be tree crowns. This paper focuses on the search procedure and on illustrating how smoothing can be used to prevent the search from becoming trapped in a local optimum. The final crown detection stage is not described in this paper but could be based on the gradient and implemented using the above described value for the line integral. Results of this paper indicate that a Fourier parametrization with only three harmonics (nine parameters) can describe the shape variation in the 2D crown projection in sufficient detail. Current ground datasets are not suitable for obtaining detection statistics such as the percentage of tree crowns detected and the number of false alarms. Better ground datasets will be needed to evaluate algorithm performance for real tree mapping situations.