Evaluation of Color Anomaly Detection in Multispectral Images For Synthetic Aperture Sensing
This work addresses real-time anomaly detection for drone-based search and rescue in dense forests, but it is incremental as it focuses on evaluating existing methods.
The paper evaluated unsupervised anomaly detection methods in multispectral images from Airborne Optical Sectioning for search and rescue missions, finding that adding a thermal channel improves performance and that HSV/HLS color spaces outperform RGB in forest environments.
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.