Texture as the basis for individual tree identification

Abstract

Recognizing plants from imagery is a complex task due to their irregular nature. In this research, three tree species, Japanese yew ( Taxus cuspidata Sieb. & Zucc.), Hicks yew ( Taxus x media), and eastern white pine ( Pinus strobus L.), were identified using their textural properties. First, the plants were separated from their backgrounds in digital images based on a combination of textural features. Textural feature values for energy, local homogeneity, and inertia were derived from the co-occurrence matrix and differed significantly between the trees and their backgrounds. Subsequently, these features were used to construct the feature space where the nearest-neighbor method was applied to discriminate trees from their backgrounds. The recognition rates for Japanese yew, Hicks yew, and eastern white pine were 87%, 93%, and 93%, respectively. The study demonstrates that the texture features selected and the methods employed satisfactorily separated the trees from their relatively complex backgrounds and effectively differentiated between the three species. This research can lead to potentially useful applications in forestry and related disciplines.