IMPROVED WATERSHED SEGMENTATION ALGORITHM FOR TREE CROWNS EXTRACTION FROM MULTI-SPECTRAL UAV-BASED AERIAL IMAGES
Keywords: Individual trees, Photogrammetric point cloud, UAV, Reconstruction, Image space, Object space
Abstract. Due to many problems such as diseases and pests, low fertility, and dehydration, trees need immediate actions to be taken in time of need. Since they are an important source of fruit, food, and nutrients consumed by humans, keeping track of the trees in orchards is a crucial issue in recent years. Today, drones equipped with multispectral cameras are used in precision agriculture, especially for monitoring and controlling trees. For this cause, two citrus orchards in Iran with an area of 9.2 and 2.67 hectares and a resolution of 3.6 and 0.68 cm were selected for the study area. In this study, First, tree extraction was conducted using four algorithms namely Local maxima, Image binarization, valley following, and watershed segmentation, and a proposed method that is based on the improvement of the watershed algorithm. This method achieved an overall accuracy of 87% and 81% in the two study regions which was higher than common methods. Secondly, the effect of the number of spectral bands on the accuracy of tree extraction was investigated. As a result, by adding of Red-Edge and NIR bands, the accuracy increased by about 5% and 7%. Therefore, experts suggested using NIR and Red-Edge bands besides RGB bands.