Hupel Tobias, Stütz Peter
Institute of Flight Systems, University of the Bundeswehr Munich, 85577 Neubiberg, Germany.
Sensors (Basel). 2023 Sep 22;23(19):8025. doi: 10.3390/s23198025.
To improve the management of multispectral sensor systems on small reconnaissance drones, this paper proposes an approach to predict the performance of a sensor band with respect to its ability to expose camouflaged targets under a given environmental context. As a reference for sensor performance, a new metric is introduced that quantifies the visibility of camouflaged targets in a particular sensor band: the Target Visibility Index (TVI). For the sensor performance prediction, several machine learning models are trained to learn the relationship between the TVI for a specific sensor band and an environmental context state extracted from the visual band by multiple image descriptors. Using a predicted measure of performance, the sensor bands are ranked according to their significance. For the training and evaluation of the performance prediction approach, a dataset featuring 853 multispectral captures and numerous camouflaged targets in different environments was created and has been made publicly available for download. The results show that the proposed approach can successfully determine the most informative sensor bands in most cases. Therefore, this performance prediction approach has great potential to improve camouflage detection performance in real-world reconnaissance scenarios by increasing the utility of each sensor band and reducing the associated workload of complex multispectral sensor systems.
为了改进小型侦察无人机上多光谱传感器系统的管理，本文提出了一种方法，用于预测传感器波段在给定环境背景下暴露伪装目标的能力方面的性能。作为传感器性能的参考，引入了一种新的指标来量化特定传感器波段中伪装目标的可见性：目标可见性指数（TVI）。对于传感器性能预测，训练了几个机器学习模型，以学习特定传感器波段的TVI与通过多个图像描述符从视觉波段提取的环境背景状态之间的关系。使用预测的性能度量，根据传感器波段的重要性对其进行排序。为了对性能预测方法进行训练和评估，创建了一个包含853次多光谱捕获以及不同环境中众多伪装目标的数据集，并已公开提供下载。结果表明，所提出的方法在大多数情况下能够成功确定最具信息性的传感器波段。因此，这种性能预测方法通过提高每个传感器波段的效用并减少复杂多光谱传感器系统的相关工作量，在实际侦察场景中提高伪装检测性能方面具有巨大潜力。
