Rule-based Classification of Airborne Laser Scanner Data for Automatic Extraction of 3D Objects in the Urban Area

• Autorzy: Bui Ngoc Quy , Le Dinh Hien , Duong Anh Quan , Nguyen Quoc Long

Identyfikatory

DOI

10.29227/IM-2021-02-01

• Języki publikacji: EN

Abstrakty

EN

LiDAR technology has been widely adopted as a proper method for land cover classification. Recently with the development of technology, LiDAR systems can now capture high-resolution multispectral bands images with high-density LiDAR point cloud simultaneously. Therefore, it opens new opportunities for more precise automatic land-use classification methods by utilizing LiDAR data. This article introduces a combining technique of point cloud classification algorithms. The algorithms include ground detection, building detection, and close point classification - the classification is based on point clouds’ attributes. The main attributes are heigh, intensity, and NDVI index calculated from 4 bands of colors extracted from multispectral images for each point. Data of the Leica City Mapper LiDAR system in an area of 80 ha in Quang Xuong town, Thanh Hoa province, Vietnam was used to deploy the classification. The data is classified into eight different types of land use consist of asphalt road, other ground, low vegetation, medium vegetation, high vegetation, building, water, and other objects. The classification workflow was implemented in the TerraSolid suite, with the result of the automation process came out with 97% overall accuracy of classification points. The classified point cloud is used in a workflow to create a 3D city model LoD2 (Level of Detail) afterward.

Słowa kluczowe

EN

point cloud; Lidar data; NDVI index; classification algorithms; 3D city

PL

chmura punktów; indeks NDVI; algorytm klasyfikacji

• Wydawca: Polskie Towarzystwo Przeróbki Kopalin
• Czasopismo: Inżynieria Mineralna
• Rocznik: 2021
• Tom: no. 2
• Strony: 103--114
• Opis fizyczny: Bibliogr. 31 poz., rys., tab., zdj.

Twórcy

autor

Bui Ngoc Quy

• Hanoi University of Mining and Geology, 18 Vien street, Hanoi, Vietnam

autor

Le Dinh Hien

• Hanoi University of Mining and Geology, 18 Vien street, Hanoi, Vietnam
• Natural resources and Environment one member co., ltd, Hanoi, Vietnam

autor

Duong Anh Quan

• Hanoi University of Mining and Geology, 18 Vien street, Hanoi, Vietnam

autor

Nguyen Quoc Long

• Hanoi University of Mining and Geology, 18 Vien street, Hanoi, Vietnam

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).