An improved approach for DSM generation from high-resolution satellite imagery

This paper develops an improved approach to digital surface model (DSM) generation from high-resolution satellite imagery (HRSI). The approach centres upon an image matching strategy that integrates feature point, grid point and edge matching algorithms within a coarse-to-fine hierarchical process. The starting point is a knowledge of precise sensor orientation, achieved in this case through bias-compensated rational polynomial coefficients (RPCs), and the DSM is sequentially constructed through a combination of the matching results for feature and grid points, and edges at different image pyramid levels. The approach is designed to produce precise, reliable and very dense DSMs which preserve information on surface discontinuities. Following a brief introduction to sensor orientation modelling, the integrated image matching algorithms and DSM generation stages are described. The proposed approach is then experimentally tested through the generation of a DSM covering the Hobart area from a stereo pair of IKONOS Geo images. The accuracy of the resulting surface model is assessed using both ground checkpoints and a lidar DSM, with the results indicating that for favourable imagery and land cover, a heighting accuracy of 2 - 4 pixels can be readily achieved. This result validates the feasibility of the developed approach for DSM production from HRSI.