Improved Salient Feature-Based Approach for Automatically Separating Photosynthetic and Nonphotosynthetic Components Within Terrestrial Lidar Point Cloud Data of Forest Canopies

Ma, Lingdi; Zheng, Guang; Eitel, Jan U. H.; Moskal, L. Monika; He, Wei; Huang, Huabing

doi:10.1109/tgrs.2015.2459716

Cited by 96 publications

(136 citation statements)

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“…For the Erytrophleum fordii tree, all classifiers' performances remained similar, indicating little effects from the number of feature used. All four classifiers showed promising results with more than 94% accuracy, which are comparable to e.g., Ma et al (2016). Although, it is noted that Ma et al (2016) worked on a more complex and littery scene.…”

Section: Experiments and Resultsmentioning

confidence: 71%

“…The assumption is that classes obey a normally distributed density function. For a binary classification problem, the continuous probability density function can be approximated as a linear combination of two probability density functions (Ma et al, 2016),…”

Section: Gaussian Mixture Modelmentioning

confidence: 99%

“…For example, understanding forest foliage profile can be of particular interest for biodiversity conservation and climate adaptation, as it affects the photosynthesis and evapotranspiration processes (Ma et al, 2016). Monitoring carbon stocks in forested ecosystems requires accurate quantification of the spatial distribution of wood volume (Levick et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Geometry based methods only use 3D coordinates of objects captured by a laser scanner. Local structurerelated saliency information are derived from 3D points and supervised machine learning methods such as Support Vector Machine (SVM) (Yun et al, 2016) and Gaussian Mixture Model (GMM) (Ma et al, 2016) are often employed to classify wood and leaf points. Some direct geometric methods were also reported (Tao et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…There is a vast need to exploit 3D geometry based approaches for separating wood and leaf points, as 3D coordinates are the most fundamental information acquired by any laser scanners. For machine learning methods, various classifiers were used in previous studies (Yun et al, 2016;Ma et al, 2016). The lack of comparable studies calls for a specific examination on how the chosen machine learning classifier and features used would influence classification results.…”

Section: Introductionmentioning

confidence: 99%

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Feasibility of Machine Learning Methods for Separating Wood And Leaf Points From Terrestrial Laser Scanning Data

Wang

Hollaus

Pfeifer

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Classification of wood and leaf components of trees is an essential prerequisite for deriving vital tree attributes, such as wood mass, leaf area index (LAI) and woody-to-total area. Laser scanning emerges to be a promising solution for such a request. Intensity based approaches are widely proposed, as different components of a tree can feature discriminatory optical properties at the operating wavelengths of a sensor system. For geometry based methods, machine learning algorithms are often used to separate wood and leaf points, by providing proper training samples. However, it remains unclear how the chosen machine learning classifier and features used would influence classification results. To this purpose, we compare four popular machine learning classifiers, namely Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF), and Gaussian Mixture Model (GMM), for separating wood and leaf points from terrestrial laser scanning (TLS) data. Two trees, an Erytrophleum fordii and a Betula pendula (silver birch) are used to test the impacts from classifier, feature set, and training samples. Our results showed that RF is the best model in terms of accuracy, and local density related features are important. Experimental results confirmed the feasibility of machine learning algorithms for the reliable classification of wood and leaf points. It is also noted that our studies are based on isolated trees. Further tests should be performed on more tree species and data from more complex environments.

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Section: Experiments and Resultsmentioning

confidence: 71%

Section: Gaussian Mixture Modelmentioning

confidence: 99%