Deep ordinal classification in forest areas using light detection and ranging point clouds

A. Morales-Martín , F. Mesas-Carrascosa, P. Gutiérrez, F. Pérez-Porras, V. Vargas, C. Hervás-Martínez

Sensors, Vol. 24(7), pp. 1-18, 2024 Indexed in JCR. Impact factor: 3.4, Position: 122/353 (Q2) in ENGINEERING, ELECTRICAL & ELECTRONIC

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).

Cite this publication

BibTex

@article{morales-martin2024deep,
    author = {Alejandro Morales-Martín and Francisco-Javier Mesas-Carrascosa and Pedro Antonio Gutiérrez and Fernando-Juan Pérez-Porras and Víctor Manuel Vargas and César Hervás-Martínez},
    title = {Deep ordinal classification in forest areas using light detection and ranging point clouds},
    journal = {Sensors},
    year = {2024},
    volume = {24},
    number = {7},
    pages = {1--18},
    doi = {10.3390/s24072168}
}

APA

Morales-Martín, A., Mesas-Carrascosa, F., Gutiérrez, P., Pérez-Porras, F., Vargas, V., Hervás-Martínez, C. (2024). Deep ordinal classification in forest areas using light detection and ranging point clouds. Sensors, 24(7), 1-18.

A. Morales-Martín (CA), F. Mesas-Carrascosa, P.A. Gutiérrez, F. Pérez-Porras, V.M. Vargas, C. Hervás-Martínez, (5/6) "Deep ordinal classification in forest areas using light detection and ranging point clouds", Sensors, Vol. 24(7), pp. 1-18, 2024. (Q2, IF: 3.4)

RIS

TY  - JOUR
T1  - Deep ordinal classification in forest areas using light detection and ranging point clouds
AU  - Morales-Martín, Alejandro
AU  - Mesas-Carrascosa, Francisco-Javier
AU  - Gutiérrez, Pedro Antonio
AU  - Pérez-Porras, Fernando-Juan
AU  - Vargas, Víctor Manuel
AU  - Hervás-Martínez, César
JO  - Sensors
VL  - 24
IS  - 7
SP  - 1
EP  - 18
PY  - 2024
DO  - 10.3390/s24072168
ER  -