Automatic building roof segmentation based on PFICA algorithm and morphological filtering from LiDAR point clouds

Saman Ghaffarian, Salar Ghaffarian, Youssef El merabet, Zineb Samir, Yassine Ruicheck

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Abstract

In this study, we propose a new approach for segmenting building roofs from Light Detection And Ranging (LiDAR) point clouds. The algorithm takes advantage of height gradients to automatically seed Purposive FastICA (PFICA) algorithm. The PFICA algorithm with a novel seeding method is implemented to detect ridge points from point clouds of building roofs. Then, 2D coordinates are used to rasterize the detected points. Eventually, morphological filtering and thinning algorithms are used to extract inner and external boundaries of the building roofs. In addition, the potential of PFICA algorithm in clustering 3D point clouds are discussed. The results obtained on a set of LiDAR point clouds demonstrate
Original languageEnglish
Title of host publication37th Asian Conference on Remote Sensing (ACRS 2016)
Subtitle of host publicationPromoting spatial data infrastructure for sustainable economic development, 17-21 October 2016, Colombo, Sri Lanka
Number of pages8
Publication statusPublished - 2016
Event37th Asian Conference on Remote Sensing, ACRS 2016: Spatial Data Infrastructure for Sustainable Development - Colombo, Sri Lanka
Duration: 17 Oct 201621 Oct 2016
Conference number: 37
http://www.acrs2016.org/

Conference

Conference37th Asian Conference on Remote Sensing, ACRS 2016
Abbreviated titleACRS
CountrySri Lanka
CityColombo
Period17/10/1621/10/16
Internet address

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