Photogrammetric point cloud generation and surface interpolation for change detection

Abstract: In recent years the science revolving image matching algorithms has gotten an upswing mostly due to its benefits in computer vision. This has led to new opportunities for photogrammetric methods to compete with LiDAR data when it comes to 3D-point clouds and generating surface models. In Sweden a project to create a high resolution national height model started in 2009 and today almost the entirety of Sweden has been scanned with LiDAR sensors. The objective for this project is to achieve a height model with high spatial resolution and high accuracy in height. As for today no update of this model is planned in the project so it’s up to each municipality or company who needs a recent height model to update themselves. This thesis aims to investigate the benefits and shortcomings of using photogrammetric measures for generating and updating surface models. Two image matching software are used, ERDAS photogrammetry and Spacemetric Keystone, to generate a 3D point cloud of a rural area in Botkyrka municipality. The point clouds are interpolated into surface models using different interpolation percentiles and different resolutions. The photogrammetric point clouds are evaluated on how well they fit a reference point cloud, the surfaces are evaluated on how they are affected by the different interpolation percentiles and image resolutions. An analysis to see if the accuracy improves when the point cloud is interpolated into a surface. The result shows that photogrammetric point clouds follows the profile of the ground well but contains a lot of noise in the forest covered areas. A lower image resolution improves the accuracy for the forest feature in the surfaces. The results also show that noise-reduction is essential to generate a surface with decent accuracy. Furthermore, the results identify problem areas in dry deciduous forest where the photogrammetric method fails to capture the forest.