Towards No Shadow: Region-based Shadow Compensation on Low-altitude Urban Aerial Images

Shahbe Mat Desa, Wan Noorshahida Mohd Isa, Petra Gomez-Krämer, Roslee M., Noramiza Hashim, Junaidi Abdullah, Aziah Ali, Zarina Che Embi, Amalina Ibrahim

Abstract: Low-altitude aerial images contain multiple shadowed regions, and a shadowed region frequently covers several non-homogenous regions of objects and surfaces. Using the information of neighboring shadow-free regions, a shadowed region can be efficiently compensated with reliable output. In this paper, using the transfer of statistical color properties, a new region-based shadow compensation approach is proposed. The proposed method demonstrates a significant improvement in the overall color mean of shadowed regions, increasing from 76.49 to 141.67, nearing the value of 172.51 observed in shadow-free regions. Additionally, the overall standard deviation of the shadowed region also improves from 31.01 to 43.29, approximately 93.68% matching the 46.21 standard deviation observed in the shadow-free region. The visual results are comparable to those achieved by state-of-the-art deep-learning methodologies and are reasonably acceptable for challenging aerial urban environments.