Quantification Of Background Clutter & Its Influence On Target Acquisition Performance Of EO Imaging Systems

With the development of electro-optical technology, imaging systems are capable of showing some internal detail on targets and some more detail in the scene at tactically significant ranges. While these changes improve the imaging range and resolution, they make the content of the scene become one of the most important factors affecting target acquisition performance. The scence content, which interferes with target acquisition performance, is defined as background clutter. Traditional performance characterization methods or models become insufficient for this type of imaging systems since they only consider a little or even no background clutter. Hence, it is necessary to improve traditional performance evaluation methods to satisfy the performance evaluation of the novel imaging system. In this paper, the following works are studied.(1) Based on the research trying to understand how human eyes perceive clutter in background, we propose a new clutter metric which estimates the degree of clutter in an image by comparisons between the target and background areas in the following aspects: intensity, statistical variance and structure. We define this metric as target structure similarity measure (TSSIM). The metric can be easily calculated and efficiently consider the human eye perception. Since requiring no specific properties about the viewing conditions, they can be easily used in various kinds of electro-optical images. Error and correlation analysis are used to experimentally validate this metric. Attempts to correlate this metric output with both experimental target detection probability and detection time yield rather good results.(2)Based on the Search₂ dataset provided by TNO Human Factors Research Institute of Netherlands, via a simple mathematical model, the target structure similarity measure is used to quantitatively describe the effect of background clutter on the human false target detection probability. Furthermore, a human false target detection decision behavior model in cluttered environments is established.(3) Based on the theory of correlation analysis and the up-to-date research findings in human visual perception, we present a new modifying method of the classic NVESD target acquisition model with more comprehensive treatment of the target signature and background clutter relationship. At the same time, the Search₂ dataset is used to compare the prediction capability for experimental target detection probabilities of the modified model to the classic one. The former shows much better predictiveperformance than the latter, and reasonability of our modification method is validated.(4) Background clutter seriously affecting the target detection performance of the machine-vision-based imaging systems, such as IR seeker. We present a robust performance evaluation technique for staring IR seekers based on signal-to-interference ratio (SIR), with the quantitative description of the background clutter emphasized. The power transfer functions of the optical system, detector and electronic are established to describe energy transmission of the signal and interference (noise and clutter) through the IR seeker, and the targets' radiant intensity statistics as well as the noise's statistical characteristics are also taken into account. In order to quantify the background clutter, we use a clutter measure based on its energy content —power spectral density (PSD). Based on this measure, a SIR is developed to analyze detection performance.