| Summary: | 碩士 === 國立臺北科技大學 === 電機工程系研究所 === 102 === Forests in Taiwan distribute vertically along the central region and can be categorized into broadleaved, mixed, and conifer forests. Terrain features make manual inspection of forests nearly impossible. By utilizing remote sensing data, the amount of field sampling could be significantly reduced. However, the visual interpretation is labor-intensive and heavily dependent on the interpreter’s experience. An automatic algorithm, called multi-level morphological active contour algorithm (MMAC) has been proposed to address these issues in 2011.
The MMAC could effectively increase recognition rate of individual tree in mountainous areas, which is the common case in Taiwan. However, the design of algorithm comes with huge computational complexity for delineation of tree crowns, which prevents it from being implemented practically in medium- or large-scale remote sensing data.
The infrastructure of parallel computing provides a solution for many algorithms with huge computational complexity. Unfortunately, its implementation was normally restricted by dependency of data and operations. Due to high operational dependency of MMAC, it is very difficult to be implemented in parallel processing to reduce its computational complexity.
This thesis will reduce the operational dependency of the MMAC algorithm by image coding method. As a result, it would realize the parallel processing of the MMAC algorithm. CUDA will be exploited as an example of parallel processing platform to demonstrate the proposed algorithm.
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