Complexity Metric of Infrared Image for Automatic Target Recognition

Abstract

Image complexity metric is an important part of automatic target recognition(ATR) performance evaluation, the relationship between infrared image complexity metric and target recognition is studied, which is important for infrared imaging system performance prediction and evaluation and the performance comparison of target recognition algorithms. Aiming at this problem, an automatic target recognition infrared image complexity metric method is proposed. Firstly, the infrared imaging mechanism is analyzed to find the main factors affecting target recognition. The image complexity is defined from the similarity degree of target and clutter and the submergence degree of target and clutter, which clarify for the influence of target recognition. To increase the universality of image complexity, the concept of feature space was introduced. Finally, the weighted processing and statistical formula F1-Score is used to combine the three indexes, the complexity of the frame image is established. The experimental results show that the proposed metric is more valid than traditional metrics, such as SV and SCR, has a strong correlation with automatic target recognition algorithm, while the values are in better agreement with the actual situation.