Multilevel T-spline Approximation for Scattered Observations with Application to Land Remote Sensing

In this contribution, we introduce a multilevel approximation method with T-splines for fitting scattered point clouds iteratively, an application to land remote sensing. This new procedure provides local surface by explicit computation of the control points and is called (MTA). It computationally efficient compared traditional global least-squares (LS) approach, which may fail when there unfavourable density from given refinement level. We validate our approach within simulated framework apply it two real datasets: (i) holes scanned terrestrial laser scanner (ii) patch on sand-dune in Netherlands. Both examples highlight potential MTA rapidly large noisy variable similar results LS approximation.