Conclusion 111

This study investigates stereo image matching methods for buildings based on stereo satellite imagery and building footprints. Because of defects in the imaging process and the weakness of stereo image matching algorithms, elevation accuracies in dense urban areas are usually lower than those in open areas. A building footprint offers valuable data to constrain the matching process, but no current methods employ building footprints directly in the matching stage. To reduce DSM error over buildings, this Chapter proposes a novel stereo image matching method integrating building footprints into the matching process of stereo satellite images.

The major contribution of this study is the proposed matching method. A building footprint map not only provides the location of a building, but also provides prior knowledge about the complicated shape and size of the building in the image. Such information can narrow the range of matching candidates and reduce computational costs. Under the framework of current DSM extraction, a new stereo image matching method is designed to integrate building footprints. The designed matching method is divided into several steps. Before image matching, stereo images are preprocessed. The preprocessing extracts building edges, refines the edge maps by eliminating edges of vegetation, and cleans trivial edges. In image matching, a building footprint is first used as a template to identify the location of the corresponding rooftop in epipolar images. Second, left and right epipolar images are matched at the given building according to their edge and intensity similarity. Third, a detailed matching is conducted to refine elevation details on rooftops. A popular semi-global stereo image matching method is used for the detailed matching. A successful left-right matching demands high matching rates on both edge and intensity matching. To effectively match buildings of different heights, a pyramid of three-level matching is designed by increasing the threshold of the maximum acceptable image parallax.

In comparison with the DSM generated from other popular commercial software, the DSM created in this study demonstrates the superiority for building rooftop elevation

images in Beijing comparing the proposed method to two commercial stereo image matching modules (ENVI and PCI). Validation by field survey indicates that the proposed method has decreased the rooftop elevation error to one third or less than the current commercial software. Furthermore, whereas the commercial software doubles the error for tall building rooftop elevations, the proposed matching method keeps elevation accuracy for low and tall buildings consistent. The comparison between a direct SGM method and this study also indicates that the proposed method can generate a more accurate building than the SGM algorithm. The DSM, after refining building areas, can apply to image true-orthorectification, 3D building reconstruction, and other applications.

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Chapter 4: An Elevation Difference Model for Building Height