Hgdnet: A Height-Hierarchy Guided Dual-Decoder Network for Single View Building Extraction and Height Estimation

作者：Chaoran Lu, Ningning Cao, Pan Zhang, Ting Liu, Baochai Peng, Guozhang Liu, Mengke Yuan, Sen Zhang, Si-Min Huang, Tao Wang · 年份：2023 · DOI：10.1109/igarss52108.2023.10282148 · 被引用次数：7 · 研究领域：Automated Road and Building Extraction、Remote Sensing and LiDAR Applications、Video Surveillance and Tracking Methods

Unifying the correlative single-view satellite image building extraction and height estimation tasks indicates a promising way to share representations and acquire generalist model for large-scale urban 3D reconstruction. However, the common spatial misalignment between building footprints and stereo-reconstructed nDSM height labels incurs degraded performance on both tasks. To address this issue, we propose a Height-hierarchy Guided Dual-decoder Network (HGDNet) to estimate building height. Under the guidance of synthesized discrete height-hierarchy nDSM, auxiliary height-hierarchical building extraction branch enhance the height estimation branch with implicit constraints, yielding an accuracy improvement of more than 6% on the DFC 2023 track2 dataset. Additional two-stage cascade architecture is adopted to achieve more accurate building extraction. Experiments on the DFC 2023 Track 2 dataset shows the superiority of the proposed method in building height estimation (δ1:0.8012), instance extraction (AP50:0.7730), and the final average score 0.7871 ranks in the first place in test phase.