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- Title
FRACTAL DIMENSION BASED SUPERVISED LEARNING FOR WOOD AND LEAF CLASSIFICATION FROM TERRESTRIAL LIDAR POINT CLOUDS.
- Authors
Hui, Z.; Xia, Y.; Nie, Y.; Chang, Y.; Hu, H.; Li, N.; He, Y.
- Abstract
Terrestrial Laser scanner has been widely used in the field of forestry. Wood-leaf separation is the fundamental step to most applications of forestry. This paper presented a robust supervised learning method for wood and leaf classification by developing four new feature vectors. Fractal dimension is first calculated to indicate the difference of regularity or roughness between wood and leaf. Zenith angle and variation are presented to distinguish trunks or branches from leaves. The adaptive axis direction of cylinder is adopted to calculate the local point density precisely. Experimental results show that the supervised learning method using the four feature vectors presented in this paper can achieve a good classification performance. Both accuracy and F1 score are higher than the ones of the method using eigen value based feature vectors.
- Subjects
FRACTAL dimensions; POINT cloud; OPTICAL scanners; LIDAR; ZENITH distance; SUPERVISED learning; FORESTS &; forestry
- Publication
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 2020, Vol 5, Issue 1, p95
- ISSN
2194-9042
- Publication type
Article
- DOI
10.5194/isprs-annals-V-1-2020-95-2020