Christoph Stahl

IR and SAR automatic target detection benchmarks

This contribution describes the results of a collaboration the objective of which was to technically validate an assessment approach for automatic target recognition (ATR) components1. The approach is intended to become a standard for component specification and acceptance test during development and procurement and includes the provision of appropriate tools and data. The collaboration was coordinated by the German Federal Office for Defense Technology and Procurement (BWB). Partners besides the BWB and the group Assessment of Fraunhofer IITB were ATR development groups of EADS Military Aircraft, EADS Dornier and Fraunhofer IITB. The ATR development group of IITB contributed ATR results and developers expertise to the collaboration while the industrial partners contributed ATR results and their expertise both from the developers and the system integrators point of view. The assessment groups responsibility was to provide task-relevant data and assessment tools, to carry out performance analyses and to document major milestones. The result of the collaboration is twofold: the validation of the assessment approach by all partners, and two approved benchmarks for specific military target detection tasks in IR and SAR images. The tasks are defined by parameters including sensor, viewing geometries, targets, background etc. The benchmarks contain IR and SAR sensor data, respectively. Truth data and assessment tools are available for performance measurement and analysis. The datasets are split into training data for ATR optimization and test data exclusively used for performance analyses during acceptance tests. Training data and assessment tools are available for ATR developers upon request. The work reported in this contribution was supported by the German Federal Office for Defense Technology and Procurement (BWB), EADS Dornier, and EADS Military Aircraft.

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

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.

Assessment of COTS IR image simulation tools for ATR development

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.

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

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.

Detection and tracking of small targets in aerial image sequences under unknown sensor motion

Automatic Target Recognition is typically based on single frame image processing. In this paper we report about our work in improving ATR performance by the exploitation of image sequences using a combination of target detection and tracking. The proposed detection/tracking system consists of three subsystems: (1) the target detection module which is based on a combination of multiresolution neural network target filters which are combined by a probabilistic belief network; (2) the sensor motion compensation system which generates a dense velocity field over the actual image frame, thus estimating the effect of the unknown sensor platform motion in image coordinates and (3) a multi-target-tracker which associates existing target tracks with new observations. By the hand of real world examples we show that the combined detection/tracking method overcomes the problem of spurious false alarms generated by the single frame target detector.