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4. CONCLUSIONS
The use of progressive densification method for ground data
classification of LiDAR cloud 3D points has a great efficiency.
The final obtained results show an important dependence on the
preset thresholds and the terrain characteristics of the scene. The
automatic selection of the thresholds is complex considering the
large variety of terrain types and characteristics of the input
data.
Techniques of classification based on the use of growth regions
are very fast and they have an easy implementation, although
the results will depend on the availability of an efficient seeds
search procedure. We propose a procedure based on the
progressive triangulation densification for the terrain regions
growing.
The combined use of both methods, progressive densification at
a preliminary stage for the seed establishment and subsequent
region growing is proposed. The approach provides goods
results in an efficient manner in scenes with different
characteristics, specially in urban and suburban areas.
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