Detection of some Tree Species from Terrestrial Laser Scanner Point Cloud Data Using Support-vector Machine and Nearest Neighborhood Algorithms

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

abolhasani h.s.; MOHAMMADZADE A.

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY Year:2020 | Volume:9 | Issue:3 Page(s): 29-40

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

471

Download:

Cites:

Information Journal Paper

Title

Detection of some Tree Species from Terrestrial Laser Scanner Point Cloud Data Using Support-vector Machine and Nearest Neighborhood Algorithms

Author(s)

abolhasani h.s. | MOHAMMADZADE A. | Issue Writer Certificate

Keywords

Point CloudQ2

Terrestrial Laser ScannerQ2

Geometric Parameters of TreeQ1

Tree SpeciesQ1

Support-vector MachineQ1

Nearest NeighborhoodQ1

Abstract

acquisition field reference data using conventional methods due to limited and time-consuming data from a single tree in recent years, to generate reference data for forest studies using Terrestrial Laser Scanner data, aerial laser scanner data, radar and Optics has become commonplace, and complete, accurate 3D data from a single tree or reference trees can be recorded. The detection and identification of Tree Species and their precise spatial information are essential for the management of natural or man-made forests, and urban vegetation covers. Terrestrial Laser Scanners are active remote sensing sensors that offer the ability for generating high-level spatial information for forestry and nature conservation applications. A Terrestrial Laser Scanner acquire detailed tree structure even in the sub-branch level. Hence, geometric information of the trees can be obtained with high accuracy from the Terrestrial Laser Scanner Point Cloud data. The proposed process in this paper is to first use the laser data points of the Terrestrial Laser Scanner of three different Tree Species: Quercus_petraea oak tree, Pinus_massoniana pine tree and Erythrophleum bean tree. geometric parameters of these trees These include extracted tree height, base canopy height, canopy height, canopy volume and tree diameter profiles. For each species, there were 12 single tree Point Cloud data of Terrestrial Laser Scanner that were processed by the reference paper provider and the leaves of the trees were considered as noise and deleted. After the geometrical parameters of these trees have been extracted, considering these geometrical parameters (9 geometrical parameters) as a feature and using support vector machine algorithms and nearest neighbor classification of these three Tree Species done. It is worth noting that the accuracy of the methods for extracting the Geometric Parameters of Trees has been evaluated by reference data that were produced non-automatically. In classification algorithm support vector machine is implemented in MATLAB programming language and RBF kernel is used for separation of three species and from each 12 Point Clouds of each species 8 Point Clouds as training data and 4 Point Clouds as test data are considered. In classifying the nearest neighbor, the value of K is empirically set when the algorithm is most accurate, and same as the SVM method of the 12 clouds available, 8 clouds are considered as training data and the rest of the clouds as test. One of the prominent goals of this study is to investigate the potential of the SVM and KNN for classificaction of Tree Species using few geometric features and few training samples. The evaluation results indicate the acceptable achieved accuracy 81% for the SVM algorithm and 74% for the KNN algorithm. In both SVM and KNN methods the accuracy of Q. petraea is 100% because the geometrical and structural features of this species are quite different from the other two species, which is clearly visualized in the images and the difference between the two The other class is completely done. The challenge of this classification relates to the other two species because they have almost identical geometrical parameters. The classification results show that the support vector machine algorithm with less training data performs better than the nearest neighbor algorithm in separating these two Tree Species.

Cites

No record.

References

No record.

Cite

APA: Copy

abolhasani, h.s., & MOHAMMADZADE, A.. (2020). Detection of some Tree Species from Terrestrial Laser Scanner Point Cloud Data Using Support-vector Machine and Nearest Neighborhood Algorithms. JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY, 9(3 ), 29-40. SID. https://sid.ir/paper/249456/en

Vancouver: Copy

abolhasani h.s., MOHAMMADZADE A.. Detection of some Tree Species from Terrestrial Laser Scanner Point Cloud Data Using Support-vector Machine and Nearest Neighborhood Algorithms. JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY[Internet]. 2020;9(3 ):29-40. Available from: https://sid.ir/paper/249456/en

IEEE: Copy

h.s. abolhasani, and A. MOHAMMADZADE, “Detection of some Tree Species from Terrestrial Laser Scanner Point Cloud Data Using Support-vector Machine and Nearest Neighborhood Algorithms,” JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY, vol. 9, no. 3 , pp. 29–40, 2020, [Online]. Available: https://sid.ir/paper/249456/en

Related Journal Papers