Jürgen Wohlfeil

Research from the field of planetary- and geoscience require investigation of geological features in harsh environments. Sampled data needs to be precisely localized spatially and in time, and to be prepared appropriately for further inspection and evaluation. In the case of thermal image data, the computer vision community has made enormous progress in the past years to combine thermal with spatial information in form of thermal 3D models. In this paper, we propose to use a camera-based hand-held sensor system to capture and georeference thermal images in rugged terrain and to prepare the data for further investigations by visualizing the thermal data in 3D. We use a global localization solution to gain fast 3D impressions of the environment and further Structure from Motion to generate detailed mid-scale models of selected interesting structures. We demonstrate our application based on a challenging dataset that we acquired in the active fumarole fields of Vulcano, Sicily.

The application of operational systems for remote sensing requires new approaches for data processing. It has to be the goal to derive user relevant information close the sensor itself and to downlink this information to a ground station or to provide them as input to an actuator of the space-borne platform. A complete automation of data processing is an essential first step for a thematic onboard data processing. In a second step, an appropriate onboard computer system has to be de-signed being able to fulfill the requirements. In this paper, standard data processing steps will be introduced correcting systematic errors during image capturing. A new hardware operating system, which is the interface between FPGA hardware and data processing algorithms, gives the opportunity to implement complex data processing modules in an effective way. As an example the derivation the camera's orientation based on data of an optical payload is described in detail. The thereby derived absolute or relative orientation is essential for high level data products. This will be illustrated by means of an onboard image matcher

Modern pixel-wise image matching algorithms like Semi-Global Matching (SGM) are able to compute high resolution digital surface models from airborne and spaceborne stereo imagery. Although image matching itself can be performed automatically, there are prerequisites, like high geometric accuracy, which are essential for ensuring the high quality of resulting surface models. Especially for line cameras, these prerequisites currently require laborious manual interaction using standard tools, which is a growing problem due to continually increasing demand for such surface models. The tedious work includes partly or fully manual selection of tie- and/or ground control points for ensuring the required accuracy of the relative orientation of images for stereo matching. It also includes masking of large water areas that seriously reduce the quality of the results. Furthermore, a good estimate of the depth range is required, since accurate estimates can seriously reduce the processing time for stereo matching. In this paper an approach is presented that allows performing all these steps fully automated. It includes very robust and precise tie point selection, enabling the accurate calculation of the images’ relative orientation via bundle adjustment. It is also shown how water masking and elevation range estimation can be performed automatically on the base of freely available SRTM data. Extensive tests with a large number of different satellite images from QuickBird and WorldView are presented as proof of the robustness and reliability of the proposed method.