Integration of Marked Point Processes and Template Matching for the identification of individual tree crowns in an urban and a wooded savanna environment in Brazil

摘要

A number of methods have been developed for the automatic identification and delineation of individual tree crowns from high spatial resolution satellite image to provide support for the management and maintenance of forests both in natural and urban environments. In this paper we present a method that integrates a Marked Point Processes (MPP) model and Template Matching (TM) to extract individual tree crowns in two tropical environments. The MPP is an extension of Markov random fields in which objects are defined by their position within a space of possible positions and their marks (e.g. shape). The MPP has been increasingly used for the recognition of objects but most implementation use an oversimplified model as mark. We argue that the MPP could take better advantage of the geometry of trees by incorporating a three-dimensional model as a mark. Conversely, TM is an approach to pattern recognition that takes the characteristics of the objects into account. Our method uses cross-correlation for determining which objects have been correctly targeted by the MPP. The correlation between the illuminated 3D crown model and the image is an inheritance from TM. The methodology was applied in synthetic images and sub-images of the WorldView satellite in two different contexts in Brazil. The results are validated by counting the correctly identified trees and by comparing their size with our interpreted version. Results are encouraging with 65 to 90% of correctly identified trees. The most difficult cases are mostly related to the existence of clustered tree crowns.