A Boundary Guided Cross Fusion Approach for Remote Sensing Image Segmentation

Remote sensing images have a variety of application prospects because of their rich information. Due to recent advances in deep learning methods, solid improvements have been made in the semantic segmentation of high-resolution remote sensing images. However, achieving precise segmentation of small and crowded objects remains a challenge. To tackle this challenging task, a Boundary Guided Cross Fusion module (BGCFM) is proposed. The Bidirectional Boundary Gate module (BBGM) is designed to provide reliable boundary information for BGCFM. Based on these two models, a remote sensing images real-time semantic segmentation network, boundary guided cross fusion network (BGCFNet), is designed. The effectiveness of the boundary-guided fusion method and the performance of BGCFNet were verified on the GID-5 dataset without pretraining. The application of the boundary-guided fusion method on the SOTA dual-branch real-time semantic segmentation network improves segmentation accuracy. BGCFNet achieves the best performance with a Mean Intersection over Union (mIoU) of 88.82%. Its inference speed is about 1.5 times that of other networks in the experiment, achieving an excellent balance between accuracy and speed.