The Use of 3D Descriptors and Intensity to Detect Trees from LiDAR Data in Urban Environment

Main Article Content

Cleber Junior Alencar
https://orcid.org/0000-0001-6562-8104
Mauricio Galo
https://orcid.org/0000-0002-0104-9960
Renato César dos Santos
https://orcid.org/0000-0003-0263-312X

Abstract

The result derived from tree detection using LiDAR data can be used in different applications such as forest management and preservation, urban planning, detection of occluded objects by tree crowns, among others. In this sense, this work aims to evaluate the applicability of 3D geometric descriptors based on eigenvalues and LiDAR intensity in tree detection. In experiments, it was analyzed the influence of neighborhood (sphere and cylinder) in the calculation of geometric attributes. From visual analyses, it was possible to notice that the use of some geometric attributes such as omnivariance, curvature, planarity and eigenentropy showed more potential in detecting trees. Considering the four selected attributes (omnivariance, curvature, planarity, eigenentropy) and intensity, the K-Means algorithm was executed to each attribute aiming to separate the point cloud into tree and non-tree. The results derived from classification were compared with reference data using quality parameters such as completeness, correctness and F-Score. Completeness values obtained with for omnivariance, eigenentropy, planarity and intensity reached values above 90%, indicating that these descriptors have high reliability for tree detection. The average correctness was around 62%, presenting a large number of false positives. When analyzing F-Score values, it was possible to verify the potential of omnivariance, computed in a spherical neighborhood, in detecting trees from LiDAR data (F-Score above 80%).

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Article Details

How to Cite
ALENCAR, C. J.; GALO, M.; SANTOS, R. C. dos. The Use of 3D Descriptors and Intensity to Detect Trees from LiDAR Data in Urban Environment. Revista Brasileira de Cartografia, [S. l.], v. 75, 2023. DOI: 10.14393/rbcv75n0a-63073. Disponível em: https://seer.ufu.br/index.php/revistabrasileiracartografia/article/view/63073. Acesso em: 22 jul. 2024.
Section
Photogrammetry

References

BARBOSA, L. J. Detecção e extração de vegetação utilizando dados lidar: Determinação de indivíduos e aglomerados. Dissertação (Mestrado em Ciências Cartográficas), Pós-Graduação em Ciências Cartográficas da Universidade Estadual Paulista, Presidente Prudente, SP, 88p, 2017.

BLOMLEY, R. et al. Shape distribution features for point cloud analysis – a geometric histogram approach on multiple scales. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. II–3, p. 9–16, 7 ago. 2014.

CHARANIYA, A. P.; MANDUCHI, R.; LODHA, S. K. Supervised Parametric Classification of Aerial LiDAR Data. 2004 CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOP. Washington, DC, USA: IEEE, 2004. Disponível em: <https://ieeexplore.ieee.org/document/1384821>. Acesso em: 5 ago. 2021

CHEN, W. et al. Airborne LiDAR Remote Sensing for Individual Tree Forest Inventory Using Trunk Detection-Aided Mean Shift Clustering Techniques. Remote Sensing, v. 10, n. 7, p. 1078, 6 jul. 2018.

DEMANTKÉ, J. et al. DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. XXXVIII-5/W12, p. 97–102, set. 2012.

DITTRICH, A.; WEINMANN, M.; HINZ, S. Analytical and numerical investigations on the accuracy and robustness of geometric features extracted from 3D point cloud data. ISPRS Journal of Photogrammetry and Remote Sensing, v. 126, p. 195–208, abr. 2017.

DONG, T. et al. Multi-layered tree crown extraction from LiDAR data using graph-based segmentation. Computers and Electronics in Agriculture, v. 170, p. 105213, mar. 2020.

DOS SANTOS, R. C. et al. Building detection from LiDAR data using entropy and the K-means concept. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. XLII-2/W13, p. 969–974, jun. 2019.

DOS SANTOS, R. C. et al. Automatic Building Change Detection Using Multi-Temporal Airborne Lidar Data. 2020 IEEE LATIN AMERICAN GRSS & ISPRS REMOTE SENSING CONFERENCE (LAGIRS). Santiago, Chile: IEEE, mar. 2020. Disponível em: <https://ieeexplore.ieee.org/document/9165628/>. Acesso em: 14 ago. 2021

DOS SANTOS, R. C. et al. The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space. Applied Geomatics, 24 abr. 2021.

EL-SHEIMY, N.; VALEO, C.; HABIB, A. Digital terrain modeling: acquisition, manipulation, and applications. Boston: Artech House, 2005.

FILIN, S.; PFEIFER, N. Neighborhood Systems for Airborne Laser Data. Photogrammetric Engineering & Remote Sensing, v. 71, n. 6, p. 743–755, 1 jun. 2005.

FIOLKA, T. et al. Distinctive 3D surface entropy features for place recognition. 2013 European Conference on Mobile Robots. 2013 EUROPEAN CONFERENCE ON MOBILE ROBOTS (ECMR). Barcelona, Catalonia, Spain: IEEE, set. 2014. Disponível em: <http://ieeexplore.ieee.org/document/6698843/>. Acesso em: 25 mar. 2021

GRUBER, T.; WILLBERG, M. Signal and error assessment of GOCE-based high resolution gravity field models. Journal of Geodetic Science, v. 9, n. 1, p. 71–86, 1 jan. 2019.

GUO, T. et al. Research on Point Cloud Power Line Segmentation and fitting algorithm. 2019 IEEE 4TH ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC). Chengdu, China: IEEE, dez. 2019. Disponível em: <https://ieeexplore.ieee.org/document/8997632/>. Acesso em: 6 ago. 2021

HAN, X.-F. et al. A comprehensive review of 3D point cloud descriptors. ArXiv, v. abs/1802.02297, 2018.

H’ROURA, J. et al. Salient Spin Images: A Descriptor for 3D Object Recognition. In: MANSOURI, A. et al. (Eds.). Image and Signal Processing. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2018. v. 10884p. 233–242.

HUI, Z. et al. Automatic DTM extraction from airborne LiDAR based on expectation-maximization. Optics & Laser Technology, v. 112, p. 43–55, abr. 2019.

ITAKURA, K.; MIYATANI, S.; HOSOI, F. Estimating Tree Structural Parameters via Automatic Tree Segmentation From LiDAR Point Cloud Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 15, p. 555–564, 2022.

JASKIERNIAK, D. et al. Individual tree detection and crown delineation from Unmanned Aircraft System (UAS) LiDAR in structurally complex mixed species eucalypt forests. ISPRS Journal of Photogrammetry and Remote Sensing, v. 171, p. 171–187, jan. 2021.

JOHNSON, A. Spin-Images A Representation for 3-D Surface Matching. PhD Thesis—Pittsburgh, PA: Carnegie Mellon University, ago. 1997.

JOHNSON, R. A.; WICHERN, D. W. Applied multivariate statistical analysis. 6th ed ed. Upper Saddle River, N.J: Pearson Prentice Hall, 2007.

JUTZI, B.; GROSS, H. Normalization Of Lidar Intensity Data Based On Range And Surface Incidence Angle. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., v. 38, jan. 2009.

KASHANI, A. et al. A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration. Sensors, v. 15, n. 11, p. 28099–28128, 6 nov. 2015.

KOCH, B.; HEYDER, U.; WEINACKER, H. Detection of Individual Tree Crowns in Airborne Lidar Data. Photogrammetric Engineering & Remote Sensing, v. 72, n. 4, p. 357–363, 1 abr. 2006.

LEE, I.; SCHENK, T. Perceptual organization of 3D surface points. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXIV, Part 3A. 2002.

LIU, H. et al. Individual tree identification using a new cluster-based approach with discrete-return airborne LiDAR data. Remote Sensing of Environment, v. 258, p. 112382, jun. 2021.

LIU, Y. et al. Classification of Airborne LIDAR Intensity Data Using Statistical Analysis and Hough Transform with Application to Power Line Corridors. 2009 DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS. Melbourne, Australia: IEEE, 2009. Disponível em: <http://ieeexplore.ieee.org/document/5384913/>. Acesso em: 5 ago. 2021

LU, X. et al. A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data. ISPRS Journal of Photogrammetry and Remote Sensing, v. 94, p. 1–12, ago. 2014.

MARTINS-NETO, R. P. Extração de variáveis dendrométricas em árvores de Pinus taeda L. a partir de

dados TLS e ALS. 2016. 187p. Dissertação (Mestrado em Engenharia Florestal). Centro de Ciências

Agroveterinárias, Universidade do Estado de Santa Catarina, Lajes, SC.

MONGUS, D.; ŽALIK, B. An efficient approach to 3D single tree-crown delineation in LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, v. 108, p. 219–233, out. 2015.

NASCIMENTO, G.A.G.; Galo, M. 2021, Aplicabilidade dos Dados Obtidos por Sistema LASER Batimétrico Aerotransportado à Cartografia Náutica: Estudo de Caso para o Arquipélago de Fernando de Noronha. Anuário do Instituto de Geociências, vol. 44: 37487. https://doi. org/10.11137/1982-3908_2021_44_37487.

OLIVEIRA, G. R. K.; GALO, M. Extração de contornos de telhados de edificações através da combinação de dados LiDAR e imagens aéreas. Revista Brasileira de Cartografia, v. 70, n. 4, p. 1378–1408, 2018.

OLIVEIRA, R. A.; GALO, M. Classificação de feições na superfície a partir de dados LiDAR e medidas de entropia e desvio padrão das altitudes. Anais do XVIII Simpósio Brasileiro de Sensoriamento Remoto, Campinas, Galoá, 2017. Disponível em: <https://proceedings.science/sbsr/papers/classificacao-de-feicoes-na-superficie-a-partir-de-dados-lidar-e-medidas-de-entropia-e-desvio-padrao-das-altitudes> Acesso em: 31 ago. 2021.

OLIVEIRA, R. A. R. de Extração de redes de distribuição aéreas de energia elétrica e identificação de regiões de contato com a vegetação a partir de dados obtidos por sistemas de varredura a LASER. Dissertação (mestrado) do Programa de Pós-Graduação em Ciências Cartográfica, Unesp - Univ. Estadual Paulista, Campus de Presidente Prudente, 2022.

ÖZDEMİR, S.; AKBULUT, Z.; KARSLI, F.; ACAR, H. Automatic extraction of trees by using multiple return properties of the lidar point cloud. International Journal of Engineering and Geosciences, v. 6, n. 1, p. 20–26, 1 fev. 2021.

RATO, D.; SANTOS, V. LIDAR based detection of road boundaries using the density of accumulated point clouds and their gradients. Robotics and Autonomous Systems, v. 138, p. 103714, abr. 2021.

RIBAS, R. P.; ELMIRO, M. A. T. Individualização de Árvores em Ambiente Florestal Nativo Utilizando Métodos de Segmentação em Modelos Digitais Produzidos a partir da Tecnologia Lidar. Revista Brasileira de Cartografia, v. 65, n. 4, 31 dez. 2013.

SHAN, J.; TOTH, C. K. Topographic LASER Ranging and Scanning - Principles and Processing. Boca Raton: CRC Press, Taylor & Francis Group, 2009.

SHAO, J. et al. Seed point set-based building roof extraction from airborne LiDAR point clouds using a top-down strategy. Automation in Construction, v. 126, p. 103660, jun. 2021.

SOKOLOVA, M.; JAPKOWICZ, N.; SZPAKOWICZ, S. Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation. In: SATTAR, A. et al. (Eds.). AI 2006: Advances in Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. v. 4304p. 1015–1021.

SOUSA, L. A. S. E; CENTENO, J. A. S. Modelagem Geométrica de Fachadas usando Nuvens de Pontos LiDAR. Revista Brasileira de Cartografia, v. 73, n. 3, p. 870–884, 8 jul. 2021.

TOMMASELLI, A. M. G. et al. Development and Assessment of a Data Set Containing Frame Images and Dense Airborne Laser Scanning Point Clouds. IEEE Geoscience and Remote Sensing Letters, v. 15, n. 2, p. 192–196, 2018.

WEINMANN, M. et al. Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers. ISPRS Journal of Photogrammetry and Remote Sensing, v. 105, p. 286–304, jul. 2015.

WEINMANN, M. et al. GEOMETRIC FEATURES AND THEIR RELEVANCE FOR 3D POINT CLOUD CLASSIFICATION. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. IV-1/W1, p. 157–164, 30 maio 2017.

WEINMANN, M.; JUTZI, B.; MALLET, C. Semantic 3D scene interpretation: A framework combining optimal neighborhood size selection with relevant features. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. II–3, p. 181–188, 7 ago. 2014.

YUNFEI, B. et al. Classification of Lidar Point Cloud and Generation of DTM from LiDAR Height and Intensity Data in Forested Area. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 37, jan. 2008.

Most read articles by the same author(s)