Use of UAV oblique imaging for the detection of individual trees in residential environments

Oblique imaging and unmanned aerial vehicles (UAV) are two state-of-the-art remote sensing (RS) techniques that are undergoing explosive development. While their synthesis means more possibilities for the applications such as urban forestry and urban greening, the related methods for data processing and information extraction, e.g. individual tree detection, are still in short supply. In order to help to fill this technical gap, this study focused on developing a new method applicable for the detection of individual trees in UAV oblique images. The planned algorithm is composed of three steps: (1) classification based on k-means clustering and RGB-based vegetation index derivation to acquire vegetation cover maps, (2) suggestion of new feature parameters by synthesizing texture and color parameters to identify vegetation distribution, and (3) individual tree detection based on marker-controlled watershed segmentation and shape analysis. The evaluations based on the images within residential environments indicated that the commission and omission errors are less than 32% and 26%, respectively. The results have basically validated the proposed method.