CADNet: Top-Down Contextual Saliency Detection Network for High Spatial Resolution Remote Sensing Image Shadow Detection

In order to improve the feature richness of remote sensing images and meet the needs of remote sensing image interpretation, shadow detection has become a hotspot in high-resolution remote sensing (HSR) research. Traditional threshold-based and machine learning-based methods do show their effectiveness, but they may not take the inherent details of the shadow into consideration, and it is difficult to cope with the saliency and intricate distribution pattern of the shadow in HSR images, which results in the lack of robustness in extracting sufficient global and local shadow contexts. To solve those problems, a top-down contextual saliency detection network (CADNet) is proposed. Compared with the traditional shadow detection network, more contextual information can be retained by the double-branch strategy of the encoder and residual dilation upsampling of the decoder in CADNet. The low-level and high-level semantic information can be combined to accurately predict the salient regions through the proposed short connection. The proposed CADNet is evaluated on a public shadow detection dataset, and the experimental results demonstrate the effectiveness of the proposed CADNet.