Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests

This paper presents an automated and effective framework for classifying airborne laser scanning (ALS) point clouds. The framework is composed of four stages: (i) step-wise point cloud segmentation, (ii) feature extraction, (iii) Random Forests (RF) based feature selection and classification, and (i...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2017-03, Vol.9 (3), p.288
Main Authors: Ni, Huan, Lin, Xiangguo, Zhang, Jixian
Format: Article
Language:English
Subjects:
airborne laser scanning
Airborne lasers
Classification
Feature extraction
feature selection
Forests
Lasers
point cloud segmentation
Post-production processing
random forests
Reverse engineering
Scanning
Segmentation
Segments
semantic
Test procedures
Three dimensional models
Topography
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Summary:This paper presents an automated and effective framework for classifying airborne laser scanning (ALS) point clouds. The framework is composed of four stages: (i) step-wise point cloud segmentation, (ii) feature extraction, (iii) Random Forests (RF) based feature selection and classification, and (iv) post-processing. First, a step-wise point cloud segmentation method is proposed to extract three kinds of segments, including planar, smooth and rough surfaces. Second, a segment, rather than an individual point, is taken as the basic processing unit to extract features. Third, RF is employed to select features and classify these segments. Finally, semantic rules are employed to optimize the classification result. Three datasets provided by Open Topography are utilized to test the proposed method. Experiments show that our method achieves a superior classification result with an overall classification accuracy larger than 91.17%, and kappa coefficient larger than 83.79%.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs9030288