Forest Canopy Image Segmentation Based on the Parametric Evolutionary Barnacle Optimization Algorithm

Forest canopy image is a necessary technical means to obtain canopy parameters, whereas image segmentation is an essential factor that affects the accurate extraction of canopy parameters. To address the limitations of forest canopy image mis-segmentation due to its complex structure, this study proposes a forest canopy image segmentation method based on the parameter evolutionary barnacle optimization algorithm (PEBMO). The PEBMO algorithm utilizes an extensive range of nonlinear incremental penis coefficients better to balance the exploration and exploitation process of the algorithm, dynamically decreasing reproduction coefficients instead of the Hardy-Weinberg law coefficients to improve the exploitation ability; the parent generation of barnacle particles (pl = 0.5) is subjected to the Chebyshev chaotic perturbation to avoid the algorithm from falling into premature maturity. Four types of canopy images were used as segmentation objects. Kapur entropy is the fitness function, and the PEBMO algorithm selects the optimal value threshold. The segmentation performance of each algorithm is comprehensively evaluated by the fitness value, standard deviation, structural similarity index value, peak signal-to-noise ratio value, and feature similarity index value. The PEBMO algorithm outperforms the comparison algorithm by 91.67%,55.56%,62.5%,69.44%, and 63.89% for each evaluation metric, respectively. The experimental results show that the PEBMO algorithm can effectively improve the segmentation accuracy and quality of forest canopy images.