Artigo Acesso aberto Revisado por pares

Classification of Photogrammetric and Airborne LiDAR Point Clouds Using Machine Learning Algorithms

2021; Multidisciplinary Digital Publishing Institute; Volume: 5; Issue: 4 Linguagem: Inglês

10.3390/drones5040104

ISSN

2504-446X

Autores

Zaide Duran, Kübra ÖZCAN, Muhammed Enes Atik,

Tópico(s)

Archaeological Research and Protection

Resumo

With the development of photogrammetry technologies, point clouds have found a wide range of use in academic and commercial areas. This situation has made it essential to extract information from point clouds. In particular, artificial intelligence applications have been used to extract information from point clouds to complex structures. Point cloud classification is also one of the leading areas where these applications are used. In this study, the classification of point clouds obtained by aerial photogrammetry and Light Detection and Ranging (LiDAR) technology belonging to the same region is performed by using machine learning. For this purpose, nine popular machine learning methods have been used. Geometric features obtained from point clouds were used for the feature spaces created for classification. Color information is also added to these in the photogrammetric point cloud. According to the LiDAR point cloud results, the highest overall accuracies were obtained as 0.96 with the Multilayer Perceptron (MLP) method. The lowest overall accuracies were obtained as 0.50 with the AdaBoost method. The method with the highest overall accuracy was achieved with the MLP (0.90) method. The lowest overall accuracy method is the GNB method with 0.25 overall accuracy.

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