IBCO-Net: Integrity-Boundary-Corner Optimization in a General Multistage Network for Building Fine Segmentation From Remote Sensing Images

Building extraction is a significant topic in high-resolution remote sensing. Insufficient integrity, irregular boundaries, and inaccurate corners remain a problem for existing methods. However, individually optimizing one of these aspects may leave problems in others. Unfortunately, few methods consider integrity, boundary, and corner simultaneously. In this study, we propose a three-stage network (IBCO-Net) incorporating integrity-boundary-corner optimization for fine segmentation of buildings. First, long-range dependent and spatial-continuous blocks (LDSCs) are plugged into the decoder to enhance building integrity. Second, the direction field correction module (DFCM) controls the overall shape of the building by learning the direction field and executing an iterative correction algorithm. Finally, the multi-strategy point refinement module (MSPRM) selects boundary and corner points for re-classification to further refine the boundary and relocate corners. And a hybrid loss function supervises IBCO-Net to optimize each stage. Comparative experiments were conducted on three datasets: the Massachusetts building dataset, the ISPRS Potsdam dataset, and the dataset of building instances of typical cities in China. We evaluated common pixel-level metrics and object-level boundary and corner metrics, with experimental results showing that IBCO-Net outperforms 8 state-of-the-art CNN and Transformer-based methods. In addition, the generality of the proposed method is demonstrated via its performance by applying 9 existing backbone networks.