Evaluating Thermal Color Palettes for Manual Human Detection in Aerial Search and Rescue Imagery

Thermal imaging plays a vital role in search and rescue, yet little is known about how color palette selection affects human recognition performance. While AI-based detection dominates current research, manual identification remains crucial in unpredictable or ethically sensitive conditions. This study evaluates three commonly used thermal palettes, White Hot, Ironbow, and Rainbow, using a recently available dataset of challenging aerial images. In an online user study with 75 participants, task time, hit accuracy, and not found responses were measured. Results show that White Hot enabled faster and more accurate performance, while Rainbow significantly hindered detection. These findings offer practical guidance for designing thermal interfaces and highlight the importance of perceptual optimization in augmented vision systems.