<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

Data publikacji: 11 lis 2020

Zakres stron: 155 - 162

Otrzymano: 30 maj 2020

Przyjęty: 10 lip 2020

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

Słowa kluczowe
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

Data publikacji: 11 lis 2020

Zakres stron: 155 - 162

Otrzymano: 30 maj 2020

Przyjęty: 10 lip 2020

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

Słowa kluczowepoint 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.

Słowa kluczowe
point cloud, classification, region growing, eigenfeature, neighbourhood