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DF-Mask R-CNN: Direction Field-Based Optimized Instance Segmentation Network for Building Instance Extraction
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DOI：10.1109/LGRS.2023.3297839.png)
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摘要
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Extracting building instances from remote sensing images has various applications. However, existing instance segmentation methods have difficulty maintaining regular and angular building boundaries, corrupting the mask quality. This letter proposes a boundary-optimized instance segmentation method based on the direction field to cope with the low boundary quality. The proposed method develops a DF-Mask head to improve the mask quality of the primary instance segmentation network (Mask R-CNN) through boundary optimization. Within the DF-Mask head, first, a gated directional context-aware module (GDCAM) improves the mask features through the directional context and a designed gating mechanism. Then a direction field rectification module (DFRM) predicts the direction field and iteratively rectifies the mask. To the best of our knowledge, this is the first time that direction field rectification has been developed to the instance segmentation method and applied to building instance extraction. Experimental results on the Vaihingen and Massachusetts datasets suggest that our method outperforms the eight state-of-the-art methods. Compared with Mask R-CNN, our method improves APs by 4.7% and 2.2% on the two datasets, respectively.
Keyword:
~Building extraction
direction field
instance segmentation
remote sensing image
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
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Boundary Shape-Preserving Model for Building Mapping From High-Resolution Remote Sensing Images高分辨率遥感影像建筑物映射的边界保形模型
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