Automatic Shadow Direction Determination using Shadow Low Gradient Direction Feature in RGB VHR Remote Sensing Images

Shadow detection provides worthwhile information for remote sensing applications, e.g. building height estimation. Shadow areas are formed in the opposite side of the sunlight radiation to tall objects, and thus solar illumination angle is required to find probable shadow areas. In the recent years, Very High Resolution (VHR) imagery provides more detailed data from the objects including shadow areas. In this regard, the motivation of this paper is to propose a reliable feature, Shadow Low Gradient Direction (SLGD), to automatically determine shadow and solar illumination direction in the VHR data. The proposed feature is based on the inherent spatial feature of fine-resolution shadow areas. Therefore, it can facilitate shadow-based operations, especially when the solar illumination information is not available in remote sensing metadata. Shadow intensity is supposed to be dependent on two factors including the surface material and sunlight illumination, which is analyzed by directional gradient values in low gradient magnitude areas. This feature considers the sunlight illumination, and ignores the material differences. The method is fully implemented on the Google Earth Engine cloud computing platform, and is evaluated on the VHR data with 0.3 m resolution. Finally, the SLGD performance is evaluated in determining shadow direction and compared in refining shadow maps.