Comparative Assessment of Five Machine Learning Algorithms for Supervised Object-Based Classification of Submerged Seagrass Beds Using High-Resolution UAS Imagery

Aris Thomasberger*, Mette Møller Nielsen, Mogens Rene Flindt, Satish Pawar, Niels Svane

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Knowledge about the spatial distribution of seagrasses is essential for coastal conservation efforts. Imagery obtained from unoccupied aerial systems (UAS) has the potential to provide such knowledge. Classifier choice and hyperparameter settings are, however, often based on timeconsuming trial-and-error procedures. The presented study has therefore investigated the performance of five machine learning algorithms, i.e., Bayes, Decision Trees (DT), Random Trees (RT), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM) when used for the object-based classification of submerged seagrasses from UAS-derived imagery. The influence of hyperparameter tuning and training sample size on the classification accuracy was tested on images obtained from different altitudes during different environmental conditions. The Bayes classifier performed well (94% OA) on images obtained during favorable environmental conditions. The DT and RT classifier performed better on low-altitude images (93% and 94% OA, respectively). The kNN classifier was outperformed on all occasions, while still producing OA between 89% and 95% in five out of eight scenarios. The SVM classifier was most sensitive to hyperparameter tuning with OAs ranging between 18% and 97%; however, it achieved the highest OAs most often. The findings of this study will help to choose the appropriate classifier and optimize related hyperparameter settings.
Original languageEnglish
Article number3600
JournalRemote Sensing
Volume15
Issue number14
Number of pages22
ISSN2072-4292
DOIs
Publication statusPublished - 2023

Keywords

  • Object-based image analysis
  • OBIA
  • Unoccupied aerial systems
  • UAS
  • Drones
  • Photogrammetry
  • Machine learning
  • Hyperparameter
  • Seagrasses
  • Coastal zone mapping
  • Limfjorden

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