The automatic recognition of individual trees in aerial images of forests based on a synthetic tree crown image model

• Main Author: Pollock, Richard
• Format: Others
• Language: English
• Published: 2009
• Online Access: http://hdl.handle.net/2429/6135

The thesis of this work is that individual tree crowns can be automatically recognized in monocular high spatial resolution optical images of scenes containing boreal or cool temperate forests in a leaved state. The thesis was advanced by developing and testing an automatic tree crown recognition procedure that is based on a model of the image formation process at the scale of an individual tree. This model provides a means of applying specific scene and image formation knowledge to the recognition task. The procedure associates instances of a three-dimensional shape description with locations in a scene image such that the descriptions estimate the visible scene extent of tree crowns that existed at the corresponding scene locations when the image was acquired. This provides an estimate of the average horizontal diameter of the vertical projection of individual recognized tree crowns, and a basis for species classification. This work makes a contribution to the overall effort to increase the level of automation in forest type mapping. This work also introduces and demonstrates the use of a pre-defined image model to support the manual acquisition of a sample of unmodelled tree crown image properties, and the use of constraints related to the spatial relationships among multiple neighbouring candidate recognition instances to resolve image interpretation conflicts. The procedure was tested with a scene of mixed uneven-aged forests in which the trees represent a wide variety of species, size, and growing conditions. The results were assessed on the basis of ground reference data and compared to those produced by human interpreters. The scene represented a greater level of difficulty than that which has been addressed by previous attempts at automating the tree crown recognition task. The test results show that the procedure was able to largely accommodate the variation represented by the test scene, but that human interpreters were better able to accommodate irregularities in tree crown form and irradiance that were caused by tight vertical and horizontal spacing of the crowns. === Science, Faculty of === Computer Science, Department of === Graduate