CLASSIFICATION OF UAV-BASED PHOTOGRAMMETRIC POINT CLOUDS OF RIVERINE SPECIES USING MACHINE LEARNING ALGORITHMS: A CASE STUDY IN THE PALANCIA RIVER, SPAIN
Autor: | Carbonell-Rivera, Juan Pedro, Estornell Cremades, Javier, Ruiz Fernández, Luis Ángel, Torralba, J., Crespo-Peremarch, P. |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
lcsh:Applied optics. Photonics
Point cloud classification UAV Structure from Motion Random forest Riverine species 010504 meteorology & atmospheric sciences UAV 0211 other engineering and technologies Point cloud Decision tree 02 engineering and technology Land cover Machine learning computer.software_genre 01 natural sciences lcsh:Technology Aleppo Pine 021101 geological & geomatics engineering 0105 earth and related environmental sciences geography geography.geographical_feature_category biology business.industry lcsh:T lcsh:TA1501-1820 15. Life on land biology.organism_classification Structure from Motion Random forest Photogrammetry Ridge lcsh:TA1-2040 Multilayer perceptron INGENIERIA CARTOGRAFICA GEODESIA Y FOTOGRAMETRIA Environmental science Riverine species Artificial intelligence business lcsh:Engineering (General). Civil engineering (General) computer Point cloud classification |
Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B2-2020, Pp 659-666 (2020) ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname |
ISSN: | 2194-9034 1682-1750 |
Popis: | The management of riverine areas is fundamental due to their great environmental importance. The fast changes that occur in these areas due to river mechanics and human pressure makes it necessary to obtain data with high temporal and spatial resolution. This study proposes a workflow to map riverine species using Unmanned Aerial Vehicle (UAV) imagery. Based on RGB point clouds, our work derived simple geometric and spectral metrics to classify an area of the public hydraulic domain of the river Palancia (Spain) in five different classes: Tamarix gallica L. (French tamarisk), Pinus halepensis Miller (Aleppo pine), Arundo donax L. (giant reed), other riverine species and ground. A total of six Machine Learning (ML) methods were evaluated: Decision Trees, Extra Trees, Multilayer Perceptron, K-Nearest Neighbors, Random Forest and Ridge. The method chosen to carry out the classification was Random Forest, which obtained a mean score cross-validation close to 0.8. Subsequently, an object-based reclassification was done to improve this result, obtaining an overall accuracy of 83.6%, and individually a producer’s accuracy of 73.8% for giant reed, 87.7% for Aleppo pine, 82.8% for French tamarisk, 93.5% for ground and 80.1% for other riverine species. Results were promising, proving the feasibility of using this cost-effective method for periodic monitoring of riverine species. In addition, the proposed workflow is easily transferable to other tasks beyond riverine species classification (e.g., green areas detection, land cover classification) opening new opportunities in the use of UAVs equipped with consumer cameras for environmental applications. |
Databáze: | OpenAIRE |
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