A coarse-to-fine boundary refinement network for building footprint extraction from remote sensing imagery

2022 
Abstract Extracting building footprints from remotely sensed imagery has long been a challenging task and is not yet fully solved. Obstructions from nearby shadows or trees, varying shapes of rooftops, omission of small buildings, and varying scale of buildings hinder existing automated models for extracting sharp building boundaries. Different reasons account for these challenges. In convolutional neural network-based methods, the down-sampling operation loses spatial details of the input images; and small buildings are omitted from the high-level features. The sheltering trees and adjacent objects shadowing may cause errors since semantic information cannot be effectively preserved. Moreover, the insufficient use of multi-scale building features causes blurry edges in the predictions for buildings with complex shapes. To address these challenges, we propose a novel coarse-to-fine boundary refinement network (CBR-Net) that accurately extracts building footprints from remote sensing imagery. Unlike the existing semantic segmentation methods that directly generate building predictions at the highest level, we designed a module that progressively refines the building prediction in a coarse-to-fine manner. In this way, the advantages of both the high-level and low-level features can be retained. We also present a novel boundary refinement (BR) module that enhances the ability of the CBR-Net model to perceive and refine building edges. The BR module refines building prediction by perceiving the direction of each pixel in a remotely sensed optical image to the center of the nearest object to which it might belong. The refined results are used as pseudo labels in a self-supervision process that increases model robustness to noisy labels or obstructions. Experimental results on three public building datasets, including the WHU building dataset, the Massachusetts building dataset, and the Inria aerial image dataset, demonstrate the effectiveness of the proposed method. In evaluation tests, CBR-Net outperformed other state-of-the-art algorithms on the three datasets by maintaining both the continuous entities and accurate boundaries of buildings. The source code of the proposed CBR-Net is available at https://github.com/HaonanGuo/CBRNet .
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