FFSSNet: Fast Fine Semantic Segmentation Network for GF-3 SAR Images in Building Areas

With the continuous development of edge computing, real-time processing of satellite images has gradually become a research hotspot. However, the current semantic segmentation methods for SAR images are suboptimal in terms of segmentation performance and inference speed. Therefore, in this article, we propose a fast fine semantic segmentation network for building regions in SAR images, called FFSSNet. The architecture of this network comprises a feature extractor, a feature reprocessing module (FRM), a multiscale feature deep fusion module (MFDFM), and an auxiliary branch. Aiming at the problem of low signal-to-noise ratio of SAR images, the network extracts multiscale features rich in context information through a feature extractor with a large receptive field. After that, through the FRM and MFDFM, the semantic information in each scale is deeply extracted and fused. This makes the feature maps contain semantic information of all scales. In addition, we design an auxiliary branch for reducing misclassification of pixels in nontarget regions. We test the network using two completely different datasets. Compared with other state-of-the-art methods, our proposed method has the better performance in terms of segmentation accuracy, inference speed, and generalization ability.