Classification of an Intertidal Reef by Machine Learning Techniques Using UAV Based RGB and Multispectral Imagery

Abstract

This study assesses machine learning methods for the classification of an intertidal reef using RGB and multispectral imagery acquired by an unmanned aerial vehicle (UAV). After the photogrammetric processing of the acquired data an orthophoto mosaic was generated, from the RGB imagery, and the reflectance of four bands (green, red, red edge and near infrared) from the multispectral data. Four machine learning classifiers were evaluated: support vector machines (SVM), artificial neural networks (ANN) naive Bayes (NB) and random forests (RF). The data was classified into four classes: sand; rock, barnacles, limpets; mussels, rock; and algae mixed. The classifiers were trained with RGB and with multispectral data. The pixel-based classification results demonstrated that when using multispectral data all classifiers overcame the performance achieved when using RGB data. NB classifier performed better in discriminating all classes and detecting submerged seaweeds. Such techniques present a valuable tool for accurately map the coastal zone.