This paper proposes a new algorithm for automatic digital terrain model (DTM) generation from high resolution CARTOSAT-1 satellite images. It consists of two major steps: generation of digital surface models (DSM) from stereo scenes and hierarchical image filtering for DTM generation. Automatic georeferencing, dense stereo matching, and interpolation into a regular grid yields a DSM. In the second step, the DSM pixels are classified into ground and nonground regions using an algorithm motivated from gray-scale image reconstruction to suppress unwanted elevated pixels. Nonground regions, i.e., 3D objects as well as outliers are iteratively separated from the ground regions. The generated DTM is qualitatively and quantitatively evaluated. Height profiles and comparisons between the generated DSM, derived DTM, and ground truth data are presented. The evaluation indicates that almost all nonground objects regardless of their size are eliminated and appropriate results are archived in hilly as well as smooth residential areas.
In this paper we present a novel image-based 3D surface reconstruction technique that incorporates both reflectance and polarisation features into a variational framework. The proposed technique is suitable for single-image and multi-image (photopolarimetric stereo) analysis. It is especially suited for the difficult task of 3D reconstruction of rough metallic surfaces. An error functional consisting of several error terms related to the measured reflectance and polarisation properties is minimised in order to obtain a 3D reconstruction of the surface. We show that the combined approach strongly increases the accuracy of the surface reconstruction result, compared to techniques based on either reflectance or polarisation alone. We perform an evaluation of the algorithm with respect to single and multiple reflectance and polarisation images of the surface, relying on synthetic ground truth data. This evaluation also reveals which polarisation features should preferably be used in the context of 3D reconstruction of rough metallic surfaces. Furthermore, we report 3D reconstruction results for a raw forged iron surface, thus showing the applicability of our method in real-world scenarios, here in the domain of industrial quality inspection.
In this paper, we will derive a phenomenological model of the bidirectional
reflectance distribution function of non-Lambertian metallic
materials typically used in industrial inspection. We will show, how
the model can be fitted to measured reflectance values and how the
fitted model can be used to determine a suitable illumination position.
Together with a given sensor pose, this illumination position can
be used to calculate the necessary shutter time, aperture, focus setting
and expected gray value to successfully perform a given visual inspection
task. The paper concludes with several example inspection tasks.