This paper suggests a method for automatic in-flight boresight calibration of pushbroom scanner images, using an on-line system with broadband data downlink and near realtime georeferencing of the pushbroom image data. Georeferencing accuracy may decrease during long image acquisition flights due to instable atmospheric conditions, which may lead to geometric changes in the flight platform. Orthorectification of (hyperspectral) pushbroom scanner data demands the knowledge of the extrinsic orientation parameters for every exposure. The most crucial parameters for the transformation of the pose obtained by the inertial navigation system (INS) into the projection center of the imaging sensor are the boresight angles. Utilizing a performant ray tracing algorithm and a digital elevation model (DEM), these parameters can be estimated even while flying in uneven and uninhabited areas. Tie points for solving an extended collinear equation are extracted automatically by the SURF algorithm

Modern mission characteristics require the use of advanced imaging sensors in reconnaissance. In particular, high spatial and high spectral resolution imaging provides promising data for many tasks such as classification and detecting objects of military relevance, such as camouflaged units or improvised explosive devices (IEDs). Especially in asymmetric warfare with highly mobile forces, intelligence, surveillance and reconnaissance (ISR) needs to be available close to real-time. This demands the use of unmanned aerial vehicles (UAVs) in combination with downlink capability. The system described in this contribution is integrated in a wing pod for ease of installation and calibration. It is designed for the real-time acquisition and analysis of hyperspectral data. The main component is a Specim AISA Eagle II hyperspectral sensor, covering the visible and near-infrared (VNIR) spectral range with a spectral resolution up to 1.2 nm and 1024 pixel across track, leading to a ground sampling distance below 1 m at typical altitudes. The push broom characteristic of the hyperspectral sensor demands an inertial navigation system (INS) for rectification and georeferencing of the image data. Additional sensors are a high resolution RGB (HR-RGB) frame camera and a thermal imaging camera. For on-line application, the data is preselected, compressed and transmitted to the ground control station (GCS) by an existing system in a second wing pod. The final result after data processing in the GCS is a hyperspectral orthorectified GeoTIFF, which is filed in the ERDAS APOLLO geographical information system. APOLLO allows remote access to the data and offers web-based analysis tools. The system is quasi-operational and was successfully tested in May 2013 in Bremerhaven, Germany

Various tasks such as urban development, terrain mapping or waterway and drainage modeling depend on digital terrain models (DTM) from large scale remote sensing data. Usually, DTM generation is a task requiring extensive manual interference. Previous attempts for automation are mostly based on determining the non-ground regions via fixed thresholds followed by smoothing operations. Thus, we propose a novel approach to automatically deduce a DTM from a digital surface model (DSM) with the aid of hyperspectral data. For this, advantages of a line scanning LiDAR system and a pushbroom hyperspectral sensor are combined to improve the result. We construct a hybrid Bayesian network (HBN), where modeled nodes can be discrete or continuous, and incorporate our already determined features. Using this network we determine probability estimates whether each point is part of terrain obstructions. While using two different sensor types supplies robust features, Bayesian networks can be automatically trained and adapted to specific scenarios such as mountainous or urban regions