Thermal building textures are used for the detection of damaged or weak spots in the insulation of building hulls. These textures can be extracted from directly geo-referenced oblique airborne infrared (IR) image sequences by projecting a 3D building model into the images. However, the direct geo-referencing is often not sufficiently ac curate and the projected 3D model does not match the structures in the image. Thus we present a technique with the main goal of finding the best fit between the 3D building model and the IR image sequence. For this purpose we correct exterior orientation via line based matching. We assign image lines to projected model lines based on the distance and angle between them. The maximum distance and maximum angle between assigned lines is given by the uncertainties in the projected lines, which is derived from the uncertainties in the 3D building model and from the uncertainties in the camera position and orientation by error propagation. Then we use the random sample consensus (RANSAC) to remove incorrect correspondences. The correspondences selected by RANSAC are adjusted using the least squares method. In the adjustment we consider both uncertainties in the model and in the image features. To evaluate the presented method we test it running the algorithm among the set of images and visually assess the improvement.     «

Thermal building textures are used for the detection of damaged or weak spots in the insulation of building hulls. These textures can be extracted from directly geo-referenced oblique airborne infrared (IR) image sequences by projecting a 3D building model into the images. However, the direct geo-referencing is often not sufficiently ac curate and the projected 3D model does not match the structures in the image. Thus we present a technique with the main goal of finding the best fit between the...     »