We aim to investigate a potential impact of boosting saturation of aerial imagery on the performance of unsupervised human detection algorithms. The study is empirical since it is based on processing photographs taken during a full year experiment in the Izerskie Mountains (southwestern Poland) by a consumer-grade Canon S110 camera mounted onboard eBee, a fixed-wing micro-unmanned aerial vehicle (UAV). In the preliminary analysis, we used a few basic color adjustments (sharpening, hue–saturation–luminance, contrast, saturation, and vibrance) to process UAV-taken photographs prior to the automated human detection with the nested k-means algorithm. We found that saturation boost is an image preprocessing method that may potentially improve the performance of human detection. In the actual analysis, we investigate only the saturation effect by employing four saturation modification schemes (two versions of enhancements of unusual colors, additive boost of saturation, and multiplicative boost of saturation) and three human detection algorithms [three-dimensional (3-D) nested k-means on RGB, two-dimensional nested k-means on hue–saturation–value, morphological operations of erosion, and dilation with thresholding]. All the studied saturation boost techniques increase detection rates of the 3-D nested k-means on RGB, with particularly meaningful improvement for images acquired in the spring. Morphological operations of erosion and dilation are not found to be skillful in detecting persons. However, their performance is improved after the initial preprocessing by the original or modified enhancement of unusual colors.