Introducing Theil-Sen estimator for sun glint correction of UAV data for coral mapping

Abstract

Despite wider applications for Unmanned Aerial Vehicle (UAV) in aquatic remote sensing, frequent sun glint in UAV acquisition often results in significant data gaps. Much research exists in the development of sun glint correction methods for airborne and satellite imagery to generate accurate coral habitat maps. Conversely, little is known about an appropriate glint correction method that can also be considered as data gap in UAV. This study compared glint correction methods for filling data gaps in UAV imagery acquired from the coral-dominated Pulau Bidong island in Peninsular Malaysia. This study proposed a simple seed pixel region growing technique that can be used in glint detection and mask development. It introduces the Theil-Sen regression glint correction (TSGC) for glint correction in UAV imagery and to achieve coral composition maps with thematic details, useful for sustainable coastal management. TSGC achieved a 25.6% greater coral classification accuracy compared to the uncorrected images.