Competing 3d Priors for Object Extraction in Remote Sensing Data

A recognition-driven variational framework was developed for automatic three dimensional object extraction from remote sensing data. The essence of the approach is to allow multiple 3D priors to compete towards recovering terrain objects’ position and 3D geometry. We are not relying, only, on the results of an unconstrained evolving surface but we are forcing our output segments to inherit their 3D shape from our prior models. Thus, instead of evolving an arbitrary surface we evolve the selected geometric shapes. The developed algorithm was tested for the task of 3D building extraction and the performed pixeland voxel-based quantitative evaluation demonstrate the potentials of the proposed approach.