Classification of airborne laser scanning point clouds based on binomial logistic regression analysis
| Autor: | Norbert Pfeifer, Christian Briese, Alain De Wulf, Peter Dorninger, Timothy Nuttens, Cornelis Stal, Philippe De Maeyer |
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| Rok vydání: | 2014 |
| Předmět: |
EXTRACTION
Binomial (polynomial) Binomial regression Feature vector MODELS Point cloud Inference computer.software_genre Logistic regression Earth and Environmental Sciences AREAS VARIANCE INFLATION FACTORS General Earth and Planetary Sciences A priori and a posteriori RECONSTRUCTION Point (geometry) ALGORITHM LIDAR DATA Data mining computer Mathematics |
| Zdroj: | INTERNATIONAL JOURNAL OF REMOTE SENSING |
| ISSN: | 1366-5901 0143-1161 |
| DOI: | 10.1080/01431161.2014.904973 |
| Popis: | This article presents a newly developed procedure for the classification of airborne laser scanning (ALS) point clouds, based on binomial logistic regression analysis. By using a feature space containing a large number of adaptable geometrical parameters, this new procedure can be applied to point clouds covering different types of topography and variable point densities. Besides, the procedure can be adapted to different user requirements. A binomial logistic model is estimated for all a priori defined classes, using a training set of manually classified points. For each point, a value is calculated defining the probability that this point belongs to a certain class. The class with the highest probability will be used for the final point classification. Besides, the use of statistical methods enables a thorough model evaluation by the implementation of well-founded inference criteria. If necessary, the interpretation of these inference analyses also enables the possible definition of more sub-classes. The use of a large number of geometrical parameters is an important advantage of this procedure in comparison with current classification algorithms. It allows more user modifications for the large variety of types of ALS point clouds, while still achieving comparable classification results. It is indeed possible to evaluate parameters as degrees of freedom and remove or add parameters as a function of the type of study area. The performance of this procedure is successfully demonstrated by classifying two different ALS point sets from an urban and a rural area. Moreover, the potential of the proposed classification procedure is explored for terrestrial data. |
| Databáze: | OpenAIRE |
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