Premium
An improved RANSAC algorithm for extracting roof planes from airborne lidar data
Author(s) -
Canaz Sevgen Sibel,
Karsli Fevzi
Publication year - 2020
Publication title -
the photogrammetric record
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 51
eISSN - 1477-9730
pISSN - 0031-868X
DOI - 10.1111/phor.12296
Subject(s) - ransac , outlier , roof , computer science , point (geometry) , plane (geometry) , artificial intelligence , boundary (topology) , algorithm , computer vision , mathematics , geography , geometry , image (mathematics) , mathematical analysis , archaeology
The extraction of building roof planes from lidar data has become a popular research topic with random sample consensus ( RANSAC ) being one of the most commonly adopted algorithms. RANSAC extracts full planes, which is problematic when there are other points outside the plane boundary but within the plane space. This study proposes an improved RANSAC (I‐ RANSAC ) algorithm by removing points that do not belong to the roof plane. I‐ RANSAC selects a random point from the extracted roof plane and then searches for its neighbours within a given threshold to identify and remove outliers. The new algorithm was tested with 14 buildings from two datasets, where quality control measures showed significant improvement over standard RANSAC .