Detection of Wet Riparian Areas using Very High Resolution Multispectral UAS Imagery Based on a Feature-based Machine Learning Algorithm

Unmanned Aerial System (UAS) imagery has enabled very high-resolution multispectral image acquisition. Detection of wet areas and classification of land cover based on these images using the Machine Learning (ML) algorithm named Random Forest (RF) is our main purpose in this paper. Very high-resolution UAS images have been used as inputs for a machine learner to access the capability of different spectral bands and spectral vegetation indices, elevation, and texture features in the classification of land cover and detection of the wet riparian area in the case study in two different epochs. There are many existing methods for the classification of land cover based on UAS images, but very high-resolution centimeter-level data are of main importance in this analysis. Outstanding results have been produced in both epochs considering three extremely accurate performance analysers. Additionally, in this research, the most decisive and effective features have been discovered to compromise accuracy and the number of effectual features.