Image matching is a key procedure in the process of generation of Digital Surface Models (DSM). We have developed a new approach for image matching and the related software package. This technique has proved its good performance in many applications. Here, we demonstrate its use in 3D tree modelling. After a brief description of our image matching technique, we show results from analogue and digital aerial images and high-resolution satellite images (IKONOS). In some cases, comparisons with manual measurements and/or airborne laser data have been performed. The evaluation of the results, qualitative and quantitative, indicate the very good performance of our matcher. Depending on the data acquisition parameters, the photogrammetric DSM can be denser than a DSM generated by laser, and its accuracy may be better than that from laser, as in these investigations. The tree canopy is well modelled, without smoothing of small details and avoiding the canopy penetration occurring with laser. Depending on the image scale, not only dense forest areas but also individual trees can be modelled.

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Commission III, WG III/2 KEY WORDS: 3D surface matching, 3D similarity transformation, strip adjustment, laser altimetry ABSTRACT: Systematic errors in point clouds acquired by airborne laser scanners, photogrammetric methods or other 3D measurement techniques need to be estimated and removed by adjustment procedures. The proposed method estimates the transformation parameters between reference surface and registration surface using a mathematical adjustment model. 3D surface matching is an extension of 2D least squares image matching. The estimation model is a typical Gauss-Markoff model and the goal is minimizing the sum of squares of the Euclidean distances between the contiguous surfaces. Besides the generic mathematical model, we also propose a concept of conjugate points rules which are suitable for special registering applications, and compare it to three typical conjugate points rules. Finally, we explain how this method can be used for the co-registration of real 3D point sets and show coregistration results based on airborne laser scanner data. Concluding results of our experiment suggest that the proposed method has a good performance of 3D surface matching, and the least normal distance rule returns the best result for the strip adjustment of airborne laser altimetry data.

Although many filter algorithms have been presented over past decades, these algorithms are usually designed for the Lidar point clouds and can’t separate the ground points from the DIM (dense image matching, DIM) point clouds derived from the oblique aerial images owing to the high density and variation of the DIM point clouds completely. To solve this problem, a new automatic filter algorithm is developed on the basis of adaptive TIN models. At first, the differences between Lidar and DIM point clouds which influence the filtering results are analysed in this paper. To avoid the influences of the plants which can’t be penetrated by the DIM point clouds in the searching seed pointes process, the algorithm makes use of the facades of buildings to get ground points located on the roads as seed points and construct the initial TIN. Then a new densification strategy is applied to deal with the problem that the densification thresholds do not change as described in other methods in each iterative process. Finally, we use the DIM point clouds located in Potsdam produced by Photo-Scan to evaluate the method proposed in this paper. The experiment results show that the method proposed in this paper can not only separate the ground points from the DIM point clouds completely but also obtain the better filter results compared with TerraSolid. 1.