Shahrood University of Technology Journal of AI and Data Mining 2322-5211 10 1 2022 01 01 Automatic Shadow Direction Determination using Shadow Low Gradient Direction Feature in RGB VHR Remote Sensing Images 53 61 2280 10.22044/jadm.2021.10705.2205 EN M. Kakooei Electrical & Computer Engineering Department, Babol Noshirvani University of Technology, Babol, Iran. Y. Baleghi Electrical & Computer Engineering Department, Babol Noshirvani University of Technology, Babol, Iran. Journal Article 2021 04 10 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 recent years, Very High Resolution (VHR) imagery provides more detailed data from 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 VHR data. The proposed feature is based on 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 VHR data with 0.3m resolution. Finally, SLGD performance is evaluated in determining shadow direction and compared in refining shadow maps. https://jad.shahroodut.ac.ir/article_2280_7d442b327cdb2e96584e134b3f70a1af.pdf