An Integrated Object and Machine Learning Approach for Tree Canopy Extraction from UAV Datasets
Author:
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Geography, Planning and Development
Link
http://link.springer.com/content/pdf/10.1007/s12524-020-01240-2.pdf
Reference18 articles.
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2. Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing Elsevier. https://doi.org/10.1016/j.isprsjprs.2009.06.004
3. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
4. Colomina, I., Molina, P., Harwin, S., Lucieer, A., Skarlatos, D., Procopiou, E., & Eisenbeiss, H. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92(November), 79–97. https://doi.org/10.1016/j.isprsjprs.2014.02.013
5. Crommelinck, S., Bennett, R., Gerke, M., Nex, F., Yang, M. Y., & Vosselman, G. (2016). Review of automatic feature extraction from high-resolution optical sensor data for UAV-based cadastral mapping. Remote Sensing, 8(8), 689. https://doi.org/10.3390/rs8080689
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