Prior-Guided Fuzzy-Aware Multibranch Network for Remote Sensing Image Segmentation

In remote sensing images (RSIs), accurate semantic segmentation faces significant challenges due to the variation in object scales, uncertain category boundaries, and complex scenes. In view of the above challenges, we propose a prior-guided fuzzy-aware multibranch network for RSI segmentation. Specifically, a prior-feature extractor (PFE) is designed to take the local features extracted by convolution structure as prior knowledge of the network. Fuzzy-aware module (FAM) is presented to perceive and refine category boundaries with fuzzy learning, which transforms the uncertainty problem into a quantitative analysis problem by establishing fuzzy relationships between neighborhood pixels. Multibranch-feature extractor (MFE) is put forward to aggregate multiscale global context information by combining positional attention and transformer. The whole network learning process is supervised by multibranch loss. We validated our method on the Gaofen Image Dataset (GID), Potsdam, and LoveDA datasets, achieving 75.58%, 75.62%, and 53.10% mean intersection over union (mIoU), respectively, which demonstrated the superiority of our method. In addition, ablation studies further demonstrate the validity of each module in the proposed method.