An Evolutionary Shadow Correction Network and a Benchmark UAV Dataset for Remote Sensing Images

Shadow correction is an important task in the analysis of high-resolution remote sensing images, as the existence of shadows reduces radiometric information and causes the changes in the energy distribution. This is especially the case when the spatial resolution is very high, as the shadows disturb the subsequent processing and applications, such as image mosaicking, classification, and segmentation. Traditional shadow correction methods are limited by the shadow detection accuracy and the available nonshaded samples in the imagery. In this article, we propose an evolutionary shadow correction network (ESCNet) and describe how we built a benchmark unmanned aerial vehicle (UAV) image dataset to achieve shadow correction directly, without shadow detection. The proposed ESCNet is made up of two subnetworks with an evolutionary relationship: a shadow removal network (SRNet) followed by a radiation adjustment network (RANet). The shadows are first removed by SRNet trained on the UAV image dataset to achieve the primary shadow-corrected image, and the global radiation is then adjusted to a sunlit-like status by RANet. Shadow detection is not required in the proposed method, which effectively overcomes the error accumulation and shadow edge artifact problem of the traditional methods. Experiments were carried out and the results were compared with those of both traditional and deep learning-based shadow correction methods, for which both qualitative and quantitative evaluations were performed. The results suggest that the proposed method shows obvious advantages in information recovery for shadow regions, and the global brightness of the corrected imagery is consistent with that of sunlit conditions.