Forestry Canopy Image Segmentation Based on Improved Tuna Swarm Optimization

Abstract

Forests play a vital role in increasing carbon sequestration in the biosphere. In recent years, segmenting forest canopy images in order to obtain various plant population parameters has become an essential means to assess the ecosystem. The objective of forest canopy image segmentation is to separate and extract sky regions from the background. This study proposes a hybrid method based on improved tuna swarm optimization (ITSO) for forestry canopy image segmentation. The symmetric cross-entropy is introduced to perform forestry canopy image thresholding by modeling the classes of an image as membership functions. In order to achieve the optimal thresholds of the forest canopy image, the entropy-solving procedure is arduous and time-consuming. In order to resolve this issue, the ITSO method was adopted to search for the most significant threshold. Meanwhile, the Tent chaotic map is used to initialize the tuna population according to the chaotic factor. The experiment is carried out on four different types of forest canopy images, with four indices (MAE, RVD, IoU, and ASD) used for quantitative analysis. The experiment’s results show that the ITSO-based segmentation method outperforms others, making it a better way to segment images of forest canopies.