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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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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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segmentation
roof
seeding
thinning
detection
seed

Cite this

Ghaffarian, S., Ghaffarian, S., El merabet, Y., Samir, Z., & Ruicheck, Y. (2016). Automatic building roof segmentation based on PFICA algorithm and morphological filtering from LiDAR point clouds. In 37th Asian Conference on Remote Sensing (ACRS 2016): Promoting spatial data infrastructure for sustainable economic development, 17-21 October 2016, Colombo, Sri Lanka
Ghaffarian, Saman ; Ghaffarian, Salar ; El merabet, Youssef ; Samir, Zineb ; Ruicheck, Yassine. / Automatic building roof segmentation based on PFICA algorithm and morphological filtering from LiDAR point clouds. 37th Asian Conference on Remote Sensing (ACRS 2016): Promoting spatial data infrastructure for sustainable economic development, 17-21 October 2016, Colombo, Sri Lanka. 2016.
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title = "Automatic building roof segmentation based on PFICA algorithm and morphological filtering from LiDAR point clouds",
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",
author = "Saman Ghaffarian and Salar Ghaffarian and {El merabet}, Youssef and Zineb Samir and Yassine Ruicheck",
year = "2016",
language = "English",
isbn = "978-1-5108-3461-3",
booktitle = "37th Asian Conference on Remote Sensing (ACRS 2016)",

}

Ghaffarian, S, Ghaffarian, S, El merabet, Y, Samir, Z & Ruicheck, Y 2016, Automatic building roof segmentation based on PFICA algorithm and morphological filtering from LiDAR point clouds. in 37th Asian Conference on Remote Sensing (ACRS 2016): Promoting spatial data infrastructure for sustainable economic development, 17-21 October 2016, Colombo, Sri Lanka. 37th Asian Conference on Remote Sensing, ACRS 2016, Colombo, Sri Lanka, 17/10/16.

Automatic building roof segmentation based on PFICA algorithm and morphological filtering from LiDAR point clouds. / Ghaffarian, Saman ; Ghaffarian, Salar; El merabet, Youssef; Samir, Zineb; Ruicheck, Yassine.

37th Asian Conference on Remote Sensing (ACRS 2016): Promoting spatial data infrastructure for sustainable economic development, 17-21 October 2016, Colombo, Sri Lanka. 2016.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

TY - GEN

T1 - Automatic building roof segmentation based on PFICA algorithm and morphological filtering from LiDAR point clouds

AU - Ghaffarian, Saman

AU - Ghaffarian, Salar

AU - El merabet, Youssef

AU - Samir, Zineb

AU - Ruicheck, Yassine

PY - 2016

Y1 - 2016

N2 - 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

AB - 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

UR - https://ezproxy2.utwente.nl/login?url=https://webapps.itc.utwente.nl/library/2016/conf/ghaffarian_aut.pdf

M3 - Conference contribution

SN - 978-1-5108-3461-3

BT - 37th Asian Conference on Remote Sensing (ACRS 2016)

ER -

Ghaffarian S, Ghaffarian S, El merabet Y, Samir Z, Ruicheck Y. Automatic building roof segmentation based on PFICA algorithm and morphological filtering from LiDAR point clouds. In 37th Asian Conference on Remote Sensing (ACRS 2016): Promoting spatial data infrastructure for sustainable economic development, 17-21 October 2016, Colombo, Sri Lanka. 2016