Aerial Point Cloud Classification Using an Alternative Approach for the Dynamic Computation of K-Nearest Neighbors

Pârvu, Iuliana Maria; Özdemir, E.; Remondino, F.

doi:10.2478/jaes-2020-0023

Abstract

The paper reports some methods to select the optimal number of neighbors and to use eigenfeatures for aerial point cloud classification. In the literature, the neighborhood selection is performed using different methods. In this paper, we propose an approach that uses the region growing algorithm. The input data is an aerial point cloud, part of the Romanian Dataset from LAKI II Project. To test our approach, we used a small dataset from the city of Marghita, Bihor County. We report the technical background for classification process and all technical details of the workflow used with insight analyses and comparisons. The work was realized within the VOLTA project (VOLTA, 2017), a RISE Marie-Curie action designed to do research and innovation activities among partners and to exchange knowledge, methods and workflows in the geospatial field.

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Aerial Point Cloud Classification Using an Alternative Approach for the Dynamic Computation of K-Nearest Neighbors

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