A Fuzzy Approach to Individual Tree Crown Delineation in Uav Based Photogrammetric Multispectral Data

Multispectral remote sensing from Unmanned Aerial Vehicle (UAV) platforms is fast becoming an economical approach to gather Very High Resolution (VHR) spatial data at the individual tree level. However, detecting and delineating tree crowns are challenging due to the intrinsic intra-class spectral variability, shadow and noise in VHR images. In addition to the unrealistic assumption that tree crowns do not overlap in forests, most state-of-the-art algorithms using photogrammetric multispectral data rely maximally on the spectral information to delineate tree crowns, resulting in crown delineation errors. The joint use of both the spectral and the spatial contextual information in multispectral data in classification mitigates the effects of intra-class spectral variance and noise, and hence has the potential to accurately delineate crowns. Thus, we proposed an approach to detect and delineate individual tree crowns by performing a Markov Random Field (MRF) based spatial-contextual modelling on a fuzzy classification framework, to achieve accurate crown delineation. The results obtained prove the proposed method to be effective.