GCCINet: Global feature capture and cross-layer information interaction network for building extraction from remote sensing imagery

The extraction of buildings from remote sensing images is a challenging task. However, existing methods are insufficiently accurate because of the diverse types of buildings, large-scale variability, and complex backgrounds in remote sensing images. There are many deficiencies of the extraction results, such as small building omission, building internal discontinuity, blurred boundary, and irregular building appearance extraction. To solve these problems, a global feature capture and cross-layer information interaction network (GCCINet) is proposed, in which the continuous atrous convolution feature enhancement module is designed to capture a larger range of multiscale building feature information by using continuous atrous convolution to generate different sizes of receptive fields, thus alleviating the problem of discontinuities and the overall appearance of irregular buildings. The global high-low feature cross-fusion module reduces the loss of local information to enhance the ability to identify small buildings through the effective cross-fusion of high-low features. The cross-layer refined fusion and boundary refinement module adopts a unique fusion method to form information fusion between different layers, obtain multiscale context information, and further refine boundaries, thus improving the capability of boundary extraction. The WHU Building Dataset and Inria Dataset are selected for validation. The results show that GCCINet performs state-of-the-art (SOTA) compared with other existing methods, and the performance of GCCINet is verified in terms of usability, interference resistance, robustness, and ablation study. Furthermore, a plug-and-play CAFE module is designed, which can introduce a few parameters to other models while improving their performance.