ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 111–118, 2022
https://doi.org/10.5194/isprs-annals-V-2-2022-111-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 111–118, 2022
https://doi.org/10.5194/isprs-annals-V-2-2022-111-2022
 
17 May 2022
17 May 2022

K-MEANS CLUSTERING BASED ON OMNIVARIANCE ATTRIBUTE FOR BUILDING DETECTION FROM AIRBORNE LIDAR DATA

R. C. dos Santos1, M. Galo1, and A. F. Habib2 R. C. dos Santos et al.
  • 1São Paulo State University – UNESP, Dept. of Cartography, Presidente Prudente, São Paulo, Brazil
  • 2Lyles School of Civil Engineering, Purdue University, West Lafayette, Indiana, USA

Keywords: Building Detection, Airborne LiDAR, Geometric Feature, Clustering, Mathematical Morphology

Abstract. Building detection is an important process in urban applications. In the last decades, 3D point clouds derived from airborne LiDAR have been widely explored. In this paper, we propose a building detection method based on K-means clustering and the omnivariance attribute derived from eigenvalues. The main contributions lie on the automatic detection without the need for training and optimal neighborhood definition for local attribute estimation. Additionally, one refinement step based on mathematical morphology (MM) operators to minimize the classification errors (commission and omission errors) is proposed. The experiments were conducted in three study areas. In general, the results indicated the potential of proposed method, presenting an average Fscore around 97%.