The random forest (RF) algorithm is among the most commonly applied machine learning algorithms in remote sensing. In this study we tested a new approach to improving the accuracy of RF algorithm when applied to multispectral derived bathymetry by increasing predictor numbers and improving hyperparameter tuning. This approach goes beyond previous work that only applied an auto-tuning hyperparameter and linearized reflectance. We tested our experimental approach on the Gili Islands of Indonesia by comparing the optimized RF to basic RF algorithms used to determine water depth from multispectral imagery. The findings of this study indicate that the optimized RF approach was particularly advantageous in high-dimension data: errors in water depth prediction accuracy improved by 46% after optimization.
|Number of pages||6|
|Journal||International Journal of Geoinformatics|
|Publication status||Published - 1 Jul 2020|