A Novel Method for Eliminating Glint in Water-Leaving Radiance from UAV Multispectral Imagery

Unmanned Aerial Vehicle (UAV) high-resolution remote sensing imagery has been used for unprecedented coastal environment monitoring with ground sampling distance and time intervals of a few centimeters and seconds, respectively. However, high spatial-time resolutions of UAV remote sensing data consist of unexpected signals from water surface level changes induced by wind-driven currents and waves. This leads to non-linear and non-stationary forms of sun and sky glints in the UAV sea surface image. Consequently, these surface glints interfere with the detection of water body reflections and objects, reducing the accuracy and usability of the measurements. This study employed Fast and Adaptive Multidimensional Empirical Mode Decomposition (FA-MEMD) to separate the spatial periodicity of time-continuous multispectral images of the sea surface from the original data and retain non-oscillatory signals called residual images. The residual images effectively represented the spatial-temporal radiance and flow variations in the water body by correcting the regions of surface glint. This study presents three key findings: First, homogeneous surface radiance data with surface glint removed from the raw image sequence was acquired using FA-MEMD. Second, the continuous surface glint removal effect is validated through water-leaving radiance (Lw-SBA) measurements obtained via the Skylight-Blocked Approach (SBA) method. Comparisons showed that R2 values for the data obtained from clear water before and after surface glint removal were 0.02 and 0.56 with RMSE values of 8.37 × 10−5 and 5.51 × 10−5 W·m−2·sr−1, respectively, indicating an improvement rate of 34.19%. Third, a comparative analysis with previous study methods demonstrated that our approach yielded spatially and temporally uniform homogeneous surface radiance data with less variability than traditional methods. The spatially and temporally synchronized residual images and the Lw-SBA data showed high similarity, confirming that the FA-MEMD technique effectively removed the surface glint from wave-induced roughness, enhancing the reliability of high-resolution UAV sea color observations.