A gradient vector flow snake based multi-level morphological active contour algorithm

Multi-level morphological active contour algorithm (MMAC), was proposed in [3-4], to incorporate mathematical morphology for tree detection and crown delineation with the average prediction error around 4% in mountainous areas. However, two major drawbacks come with the design of MMAC algorithm, which are huge computational complexity and certain level of false alarm or omission rate. The paper provides a solution for two major problems by proposing a gradient vector flow (GVF) snake based MMAC algorithm. The GVF snake model took advantage of the gradient vector force of the LiDAR image and expand the boundary of each blob to approximate the real crown of each forest candidate. According to the experimental studies, the proposed algorithm could help improve the computational complexity and detection rate of the original MMAC algorithm effectively.