The nested k-means method: A new approach for detecting lost persons in aerial images acquired by unmanned aerial vehicles

Abstract

A new method, named as the nested k-means, for detecting a person captured in aerial images acquired by an unmanned aerial vehicle (UAV), is presented. The nested k-means method is used in a newly built system that supports search and rescue (SAR) activities through processing of aerial photographs taken in visible light spectra (red-green-blue channels, RGB). First, the k-means classification is utilized to identify clusters of colors in a three-dimensional space (RGB). Second, the k-means method is used to verify if the automatically selected class of colors is concurrently spatially clustered in a two-dimensional space (easting-northing, EN), and has human-size area. The UAV images were acquired during the field campaign carried out in the Izerskie Mountains (SW Poland). The experiment aimed to observe several persons using an RGB camera, in spring and winter, during various periods of day, in uncovered terrain and sparse forest. It was found that the nested k-means method has a considerable potential for detecting a person lost in the wilderness and allows to reduce area to be searched to 4.4 and 7.3% in spring and winter, respectively. In winter, land cover influences the performance of the nested k-means method, with better skills in sparse forest than in the uncovered terrain. In spring, such a relationship does not hold. The nested k-means method may provide the SAR teams with a tool for near real-time detection of a person and, as a consequence, to reduce search area to approximately 0.5–7.3% of total terrain to be visited, depending on season and land cover.