  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.
