论文标题
在多光谱图像中评估颜色异常检测的合成光圈传感
Evaluation of Color Anomaly Detection in Multispectral Images For Synthetic Aperture Sensing
论文作者
论文摘要
在本文中，我们评估了用独立于波长的合成光圈传感技术获得的多光谱图像中无监督的异常检测方法，称为空气寄生光学分段（AOS）。侧重于搜救任务，这些搜救任务将无人机用于在茂密的森林中找到失踪或受伤的人，并且需要实时操作，我们评估了这些方法的运行时与质量。此外，我们显示通常在视觉范围内运行的颜色异常检测方法始终受益于额外的远红外（热）通道。我们还表明，即使没有其他热带，视觉范围内的颜色空间的选择已经对检测结果产生了影响。诸如HSV和HLS之类的颜色空间具有胜过广泛使用的RGB颜色空间的潜力，尤其是在颜色异常检测中用于森林样环境时。
In this article, we evaluate unsupervised anomaly detection methods in multispectral images obtained with a wavelength-independent synthetic aperture sensing technique, called Airborne Optical Sectioning (AOS). With a focus on search and rescue missions that apply drones to locate missing or injured persons in dense forest and require real-time operation, we evaluate runtime vs. quality of these methods. Furthermore, we show that color anomaly detection methods that normally operate in the visual range always benefit from an additional far infrared (thermal) channel. We also show that, even without additional thermal bands, the choice of color space in the visual range already has an impact on the detection results. Color spaces like HSV and HLS have the potential to outperform the widely used RGB color space, especially when color anomaly detection is used for forest-like environments.
