Dense Image Matching in Airborne Video Sequences

The use of airborne video data is gaining increasingly attention in the photogrammetric community. This interest is driven by the availability of low-cost sensor platforms like UAV and low-cost sensors such as digital (video) consumer cameras. Moreover, a wide range of applications are related to this kind of sensor data, e.g. fast mapping in case of disasters, where geometric and semantic information on a particular scene has to be captured within a small timeframe. The advantage of video data against wide baseline images is that tracking algorithms can be used to derive highly redundant tie point information in a fully automatic manner. One drawback is that due to the reduced resolution and only short exposure time, the image quality is worse compared to the quality provided by mapping cameras. However, the many-fold overlapping enables the use of multiframe super resolution techniques to obtain higher quality textures. In this paper the focus lies on the dense surface reconstruction using airborne video sequences. The first step in the approach consists of retrieving the structure and motion of the cameras, also incorporating geometric knowledge on the scene. In the subsequent step a dense surface reconstruction is applied. First, appropriate image combinations for the stereo matching are selected. After rectification, the Semi-Global Matching technique is applied, using the Mutual Information approach for retrieving local energy costs. After the matches are linked, super resolution images are computed and 3D point clouds are derived by forward intersection. The results for two datasets show that the super resolution images have a higher nominal resolution than the original ones. As the accuracy of the forward intersection depends on the actual image acquisition parameters, the unfiltered 3D point cloud could be noisy. Therefore, some further improvements for the 3D point coordinates are identitied.