The United States Forest Service (USFS) is developing software systems to evaluate forest resources with respect to qualities such as scenic beauty and vegetation structure. Such evaluations usually involve a large amount of human labor. In this thesis, I will discuss the design and implementation of a digital image segmentation system, and how to apply it to analyze forest images so that automated forest resource evaluation can be achieved. The first major contribution of the thesis is the evaluation of various feature design schemes for segmenting forest images. The other major contribution of this thesis is the development of a pattern recognition-based image segmentation algorithm. The best system performance was a 61.4% block classification error rate, achieved by combining color histograms with entropy. This performance is better than that obtained by an ?intelligent? guess based on prior knowledge about the categories under study, which is 68.0%.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-5307 |
| Date | 04 August 2001 |
| Creators | Long, Zhiling |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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