Optimization of DTM interpolation using SFS with single satellite imagery
Digital terrain models (DTMs) in the present context are simply regular grids of elevation measurements over the land surface. DTMs are mainly extracted by applying the technique of stereo measurements to images available from aerial photography and/or remote sensing. Enormous amounts of local and global DTM data with different specifications are now available. However, numerous geoscience and engineering applications need denser and more accurate DTM data. Collecting additional height data in the field, if not impossible, is either expensive or time consuming or both. Stereo aerial or satellite imagery is often unavailable and very expensive to acquire. Interpolation techniques are fast and cheap, but have their own inherent difficulties and problems, especially in rough terrain. Advanced space technology has provided much single ( if not stereo) high-resolution satellite images almost worldwide. Besides, shape from shading (SFS) is one of the methods to derive the geometric information about the objects from the analysis of the monocular images. This paper discusses the idea of using the SFS method with single high resolution imagery to optimize the interpolation techniques used in densifying regular grids of heights. Three different methodologies are briefly explained and then implemented with both simulated and real data. Numerical results are briefly discussed and a short discussion on how to make the computations more efficient will be presented. The implemented algorithms show that one can easily take advantage of parallel processing techniques to speed up the highly demanding computations involved in this application. Finally, a few remarks and conclusions are drawn.
interpolation digital terrain model remote sensing shape from shading parallel processing densification classification albedo irradiance model shape index