During a previous technology programme, a simple landscape and complex target geometries were modelled and
demonstrated in a COTS infrared (IR) simulation tool. A preliminary assessment of training-based ATR on real and
synthetic imagery was performed, which was presented at SPIE D&S in 2005.
The current technology programme has assessed model-based ATR on real and synthetic IR imagery for a 5-class case.
Real IR imagery was recorded during a flight campaign. A complex landscape and complex targets were modelled and
simulated in a wide variety of conditions in the IR simulation tool.
A survey was conducted regarding the current state-of-the-art of model-based ATR approaches. Another survey
concerning contour extraction methods for ATR was performed. The best ATR algorithms and contour extraction
methods were selected from the survey results. These algorithms were implemented for a multi-class ATR case and
adapted to work on the characteristics of IR imagery. The algorithms were benchmarked and compared on the simulated
and recorded IR imagery using classical measures. A process for performance assessment of multi-class ATR methods
was defined according to an ATR benchmarking concept developed by the German Fraunhofer Research Institute. The
assessment was then conducted on the algorithms using a multi-class evaluation approach.
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.
Following the tendency of increasingly using imaging sensors in military aircraft, future combat airplane pilots will need onboard artificial intelligence for aiding them in image interpretation and target designation.
This document presents a system which is able to simulate high-resolution artificial SAR imagery and thereby facilitates automatic target recognition (ATR) algorithm development. The system provides a comprehensive interface that allows dynamically requesting imagery depending on the location and heading of a simulated carrier platform. Landscapes, structures and target signatures are generated based on digital terrain data and target models.
An assessment of dissimilar database preparations for sensor simulation was done with respect to the different properties of SAR imaging compared to optical imaging. The document presents selected results for specific landscape elements. Post-processing algorithms for overcoming weaknesses of digital terrain databases and improving image realism are presented.
Simulated sensor imagery is useful in a wide range of applications, two of which are training of ATR algorithms and sensor simulation in flight simulation environments.
Using an existing ATR method as an example, the applicability and the influences of synthetic imagery on ATR training are shown and first approaches on how to validate the correctness of the imagery are explained. The integration of the system into a flight simulator in the context of interfacing and control topics serves as a concluding example.