An omni-scale global–local aware network for shadow extraction in remote sensing imagery

Shadows not only reduce image quality but also interfere with image interpretation, and accurate shadow extraction is the key to improving remote sensing image utilization. However, complex features lead to shadow extraction difficulties in remote sensing imagery. In this paper, an omni-scale global–local aware network (OGLANet) is proposed by analyzing the typical characteristics of shadows in remote sensing images. First, we establish a global–local aware module (GLAM) for fully extracting shadow features to solve the problem regarding the insufficient ability to control global and local network features. Second, the detailed and semantic information of shadows exists on different scales. We propose a dense feature fusion module (DFFM) between the encoder and decoder so that the detailed information can be restored in the decoding stage while retaining the semantic information. Finally, to solve the extreme scale differences of shadows, an omni-scale aggregation module (OAM) is established; this module can obtain more refined results in the prediction stage. To prove the effectiveness of our method, we compare it with state-of-the-art (SOTA) deep learning models proposed in recent studies on the same dataset. The results show that our method achieves higher accuracy and that the proposed OGLANet exhibits higher robustness and transferability than other methods.