LAUSR

Salient object detection (SOD) in optical remote sensing images (RSIs) is a valuable and challenging task. Although many SOD methods for RSIs have been proposed, there are still some problems, such as insufficient feature extraction, relatively worse detection results for tiny objects, and serious interference by background clutter and lighting shadows. In this article, we propose a novel multiscale feature enhancement network (MFENet) for SOD in optical RSIs. Specifically, MFENet first uses the global feature perception (GFP) module to extract the multiscale and global feature information of salient object regions. Second, the global features are enhanced from the channel and spatial dimensions by using the mixed feature enhancement (MFE) module. Third, the semantic feature guidance (SFG) module is constructed to guide the model to obtain fine-grained semantic feature information. Finally, the boundary optimization module (BOM) is designed to refine the boundary features and obtain accurate saliency detection results. To evaluate the performance of MFENet, extensive experiments are conducted on the publicly available ORSSD, EORSSD, and ORSI-4199 datasets. The quantitative and qualitative results show that our method outperforms the existing state-of-the-art SOD methods in several evaluation metrics and exhibits better inference speed (FPS) and calculation parameters. The code and results of MFENet are available at https://github.com/darkseid-arch/MFENet.