Slice-to-slice context transfer and uncertain region calibration network for shadow detection in remote sensing imagery.

Although current methods based on deep learning (DL) have been widely employed in shadow detection tasks, the cluttered background and complex shadow features in remote sensing images (RSIs) make shadow detection still a challenging task. In this paper, we apply a neural network combined with a distance transformation algorithm to RSI shadow detection for the first time and propose a novel slice-to-slice context transfer and uncertainty region calibration network (SCUCNet). First, an interslice context semantic transfer (SCT) module is proposed to explore the connections among spatial pixel information and extract sufficient global contextual information via interslice feature transfer. This process helps alleviate the interference of multiscale differences and complex backgrounds on the detection results. Second, to further optimize the initial output mapping, calibrate the uncertainty region, and enhance the feature representation, we propose a plug-and-play uncertainty region awareness and calibration (URAC) module combined with a distance transform algorithm, which introduces only 0.09 M parameters. The quantitative results show that the proposed model outperforms state-of-the-art methods for shadow detection in RSIs, achieving an Intersection over Union (IoU) of 88.33% on the aerial imagery dataset for shadow detection (AISD). In addition, extensive experiments and visualizations further prove the validity and interpretability of our method, and the proposed method can effectively mitigate the interference caused by multiscale differences, morphological diversity, spectral similarity and heterogeneity. [ABSTRACT FROM AUTHOR]