Individual tree crown detection in sub-meter satellite imagery using Marked Point Processes and a geometrical-optical model

Abstract

This article describes a new algorithm for the detection and delineation of tree crowns using optical sub-meter resolution satellite images. The algorithm focuses on detecting individual semi-isolated trees in a variety of environments defined as trees outside forests (TOF). The concept of Marked Point Processes (MPPs), which alternates phases of “birth” and “death” iterations to satisfy a density factor was used as a theoretical basis. The “mark” in the MPP represents the object being sought. Unlike most applications of MPP to object recognition, the mark used in our algorithm is computed from a 3D geometrical optical model artificially lit using the same illumination parameters as the image itself. Because trees differ in size, the process also incorporates a tree crown radius variable. The algorithm is tested on four sub-meter satellite images, each in a different environment. Validation was performed on both detection and delineation. The detection was based on tree crown counting and yielded an accuracy ranging from 0.81 to 0.95 for the four images. The delineation accuracy was estimated based on the crown pixel count and yielded an accuracy of ≈ 0.63 (0.57–0.72).