ABSTRACT
Motivation -- To study the effects of the reliability of ATR (Automatic Target Recognition) designations on the performance of expert image analysts of SAR (Synthetic Aperture Radar) images.
Research approach -- A psychophysical study of the performance of 12 expert analysts of SAR images.
Findings -- Analyst performance was influenced by ATR reliability. Higher reliabilities yielded higher hit rates and higher false alarm rates, and low reliabilities the opposite results. This and a signal detection theory analysis indicate that ATR reliability affects the response criterion and not performance per se. (But see Discussion).
Research Implications -- The fact that the reliability of items designated by the ATR system affected the criterion of the analysts has important implications. The tendency to mark more items that were designated by the ATR as being true targets should improve the overall performance of analysts working with state-of-the-art ATR systems (see Discussion).
Originality/Value -- The research systematically manipulated the reliability levels of simulated ATR systems, and measured their influence on the performance of human analysts. In this context reliability rate means what percentage of the designated items by an ATR system are actually correct targets. Each ATR block was coupled with a similar non-ATR block, a design that aimed to extract the added value of the ATR system to the performance of the human analysts. In addition, a complete within subjects design was used. This procedure provided a good basis for comparing the different conditions in the experiment.
Take away message -- While developing an ATR system, one should provide the image analysts with valid assessments of the system's reliability.
- Davidson, H., & Wickens, C. D. (1999). Rotorcraft hazard cueing: The effect of attention and trust. (Technical Report ARL-99-5/NASA-99-1). Savoy, IL: University of Illinois, Institute of Aviation, Aviation Research Laboratory.Google Scholar
- Kuperman, G. G. & Sobel, A. L. (1993). Systems engineering/human factors approach to human machine interface design for aided target acquisition. Technical Report, United States Department of Energy (DE), 22p.Google Scholar
- Parasuraman, R. & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230--253.Google ScholarCross Ref
- Riley, V. (1996). Operator reliance on automation: Theory and data. In Parasuraman & Moulou (Eds.), Automation and Human Performance (pp. 19--35). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
- Wickens, C. D. (2001). Attention to safety and the psychology of surprise. Presented at the 11th International Symposium on Aviation Psychology, Columbus, OH: The Ohaio State University.Google Scholar
- The effects of the reliability of an automatic target recognition system on image analyst performance
Recommendations
Scattering-Based Tomography for HRR and SAR Prediction
This paper introduces a method for predicting HRR radar signatures and SAR images by creating a parametric three-dimensional scattering model from existing measured or model-based HRR signatures and/or SAR images. The method identifies potential three-...
Physics-Based SAR Modeling and Simulation for Large-Scale Data Generation of Multi-platform Vehicles for Deep Learning-Based ATR
Dynamic Data Driven Applications SystemsAbstractOne critical challenge of Automatic Target Recognition (ATR) systems are that of effective modeling and interpretation of sensory data obtained under constantly changing dynamic environments. In this paper, we address physics-based modeling and ...
Dynamic Transfer Learning from Physics-Based Simulated SAR Imagery for Automatic Target Recognition
Dynamic Data Driven Applications SystemsAbstractIn this paper, we present a two-step deep learning technique in support of DDDAS for achievement of robust ATR via transfer learning using simulated SAR imagery. The first Deep Learning (DL) model performs noise suppression of input SAR images via ...
Comments