<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

National Center of Cartography, Cartography and Photogrammetry Department, 012101 / Doctoral School

Technical University of Civil Engineering of Bucharest

Space Center, Skolkovo Institute of Technology (SKOLTECH)

Journal of Applied Engineering Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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...

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

Published Online: Nov 11, 2020

Page range: 155 - 162

Received: May 30, 2020

Accepted: Jul 10, 2020

DOI: https://doi.org/10.2478/jaes-2020-0023

Keywords
point cloud, classification, region growing, eigenfeature, neighbourhood

© 2020 Iuliana Maria Pârvu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Published Online: Nov 11, 2020

Page range: 155 - 162

Received: May 30, 2020

Accepted: Jul 10, 2020

DOI: https://doi.org/10.2478/jaes-2020-0023

Keywordspoint cloud, classification, region growing, eigenfeature, neighbourhood

© 2020 Iuliana Maria Pârvu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
point cloud, classification, region growing, eigenfeature, neighbourhood