Mr. Heiko Seidel Profile

Heiko Seidel

Software Engineer at Airbus Defence and Space Air Systems

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Publications (3)

Proceedings Article | 16 April 2008 Paper

Assessment of model-based automatic target recognition on recorded and simulated infrared imagery

Heiko Seidel, Christoph Stahl, Wolfgang Ensinger, Frode Bjerkeli, Paal Skaaren-Fystro, Kirsten Rosseland, Per Inge Jensen

Proceedings Volume 6967, 696719 (2008) https://doi.org/10.1117/12.776663

KEYWORDS: Infrared imaging, Databases, Automatic target recognition, Detection and tracking algorithms, Model-based design, Particle filters, Performance modeling, Data modeling, Infrared radiation, Thermal modeling

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Proceedings Article | 19 May 2005 Paper

Assessment of COTS IR image simulation tools for ATR development

Heiko Seidel, Christoph Stahl, Frode Bjerkeli, Paal Skaaren-Fystro

Proceedings Volume 5807, (2005) https://doi.org/10.1117/12.602461

KEYWORDS: Automatic target recognition, Infrared imaging, Commercial off the shelf technology, Image sensors, Sensors, Thermography, Thermal modeling, Performance modeling, Defense and security, Infrared sensors

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Following the tendency of increased use of imaging sensors in military aircraft, future fighter pilots will need onboard artificial intelligence e.g. ATR for aiding them in image interpretation and target designation. The European Aeronautic Defence and Space Company (EADS) in Germany has developed an advanced method for automatic target recognition (ATR) which is based on adaptive neural networks. This ATR method can assist the crew of military aircraft like the Eurofighter in sensor image monitoring and thereby reduce the workload in the cockpit and increase the mission efficiency. The EADS ATR approach can be adapted for imagery of visual, infrared and SAR sensors because of the training-based classifiers of the ATR method. For the optimal adaptation of these classifiers they have to be trained with appropriate and sufficient image data. The training images must show the target objects from different aspect angles, ranges, environmental conditions, etc. Incomplete training sets lead to a degradation of classifier performance. Additionally, ground truth information i.e. scenario conditions like class type and position of targets is necessary for the optimal adaptation of the ATR method. In Summer 2003, EADS started a cooperation with Kongsberg Defence & Aerospace (KDA) from Norway. The EADS/KDA approach is to provide additional image data sets for training-based ATR through IR image simulation. The joint study aims to investigate the benefits of enhancing incomplete training sets for classifier adaptation by simulated synthetic imagery. EADS/KDA identified the requirements of a commercial-off-the-shelf IR simulation tool capable of delivering appropriate synthetic imagery for ATR development. A market study of available IR simulation tools and suppliers was performed. After that the most promising tool was benchmarked according to several criteria e.g. thermal emission model, sensor model, targets model, non-radiometric image features etc., resulting in a recommendation. The synthetic image data that are used for the investigation are generated using the recommended tool. Within the scope of this study, ATR performance on IR imagery using classifiers trained on real, synthetic and mixed image sets was evaluated. The performance of the adapted classifiers is assessed using recorded IR imagery with known ground-truth and recommendations are given for the use of COTS IR image simulation tools for ATR development.

Proceedings Article | 21 September 2004 Paper

Dynamic generation of artificial HRSAR imagery for ATR development and cockpit simulation

Heiko Seidel, Christoph Stahl, Peter Knappe, Peter Hurst

Proceedings Volume 5426, (2004) https://doi.org/10.1117/12.541273

KEYWORDS: Synthetic aperture radar, Databases, Automatic target recognition, Device simulation, Sensors, Data modeling, Computer simulations, 3D modeling, Image sensors, Solid modeling

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