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Pollock model was one of the popular template matching algorithms, which constructs a three-dimensional description of each individual tree-crown envelope. Unfortunately, it is always challenging to determine parameters since each tree has different grown condition. This paper proposed an iterative least square fitting algorithm to form a Pollock model best fitting each individual tree for tree detection...
Multi-level morphological active contour algorithm (MMAC), was proposed in [3-4], to incorporate mathematical morphology for tree detection and crown delineation with the average prediction error around 4% in mountainous areas. However, two major drawbacks come with the design of MMAC algorithm, which are huge computational complexity and certain level of false alarm or omission rate. The paper provides...
Forests in mountainous areas normally contain a diverse variety, including tropical rain forests, subtropical evergreen forests, temperate forests, and alpine cold forests. These forests distribute vertically along the central region and can be categorized into broadleaved, mixed, and conifer forests. Terrain features in mountainous areas make manual inspection of forests both labor-intensive and...
Multi-level Morphological Active Contour algorithm (MMAC) had been proposed to effectively increase recognition rate of individual tree in mountainous areas. However, it was specifically designed for LiDAR CHM data only, which make the algorithm incompatible with any other type of remote sensing data. To relieve constraints of MMAC this manuscript proposed a spectral-based MMAC (SB-MMAC), which retains...
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