A Shadow Detection Algorithm Based on Multiscale Spatial Attention Mechanism for Aerial Remote Sensing Images

Automated shadow detection is an important research problem in the field of remote sensing image processing. The shadow regions seriously affect the interpretation of the remote sensing images. However, the existing methods have poor detection effect for small shadow regions, and the ability to distinguish between weakly illuminated regions and shadow regions is insufficient. For this reason, we propose a shadow detection network based on multiscale spatial attention mechanism for aerial remote sensing images, called MSASDNet. First, the backbone based on residual block is employed to extract the preliminary features of the input image. Then, we design a multiscale feature extraction module based on the spatial attention mechanism to extract multiscale features with spatial attention information, which can suppress the influence of complex nonshadow regions on the detection and improve the detection ability of small shadow regions. Finally, the decoder structure based on deconvolution is used to predict the shadow mask from the combined feature. Experiments performed on the Aerial Imagery dataset for Shadow Detection (AISD) dataset demonstrate the superiority of MSASDNet in terms of quantitative and qualitative comparison with several state-of-the-art methods. The code will release soon in https://github.com/HITLDY/MSASDNet.