Deep Ordinal Classification in Forest Areas Using Light Detection and Ranging Point Clouds

Author:

Morales-Martín Alejandro1ORCID,Mesas-Carrascosa Francisco-Javier2ORCID,Gutiérrez Pedro Antonio1ORCID,Pérez-Porras Fernando-Juan2ORCID,Vargas Víctor Manuel1ORCID,Hervás-Martínez César1ORCID

Affiliation:

1. Department of Computer Science and Numerical Analysis, University of Córdoba, Campus de Rabanales, 14071 Córdoba, Spain

2. Department of Graphic Engineering and Geomatics, University of Córdoba, Campus de Rabanales, 14071 Córdoba, Spain

Abstract

Recent advances in Deep Learning and aerial Light Detection And Ranging (LiDAR) have offered the possibility of refining the classification and segmentation of 3D point clouds to contribute to the monitoring of complex environments. In this context, the present study focuses on developing an ordinal classification model in forest areas where LiDAR point clouds can be classified into four distinct ordinal classes: ground, low vegetation, medium vegetation, and high vegetation. To do so, an effective soft labeling technique based on a novel proposed generalized exponential function (CE-GE) is applied to the PointNet network architecture. Statistical analyses based on Kolmogorov–Smirnov and Student’s t-test reveal that the CE-GE method achieves the best results for all the evaluation metrics compared to other methodologies. Regarding the confusion matrices of the best alternative conceived and the standard categorical cross-entropy method, the smoothed ordinal classification obtains a more consistent classification compared to the nominal approach. Thus, the proposed methodology significantly improves the point-by-point classification of PointNet, reducing the errors in distinguishing between the middle classes (low vegetation and medium vegetation).

Funder

European Commission

ENIA International Chair in Agriculture, University of Córdoba

Agencia Española de Investigación

European Social Fund (ESF), Operational Programme for Youth Employment

FPU Predoctoral Program of the Spanish Ministry of Science, Innovation and Universities

Publisher

MDPI AG

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