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Building Instance Extraction Method Based on Improved Hybrid Task Cascade
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DOI：10.1109/LGRS.2021.3060960.png)
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Automatic building extraction from remote sensing imagery is crucial to urban construction and management. To address the main challenges of diverse building scale and appearance, this letter proposes an automatic building instance extraction method based on an improved hybrid task cascade (HTC). Our method consists of three components by obtaining high-resolution representation, defining guided anchor, and forming focal loss to boost the adaptability of automatic building instance extraction. Comprehensive experimental results on WHU aerial building data set demonstrated that compared with the mainstream Mask R-CNN method, our method increased AP and AR in bounding box branch and mask branch by 9.8%-6.5% and 10.7%-8.0% respectively, especially AP(S) and AP(L) in the two branches by 10.1%-6.9% and 3.4%-2.4%, respectively. We evaluated the effectiveness and complexity of these components separately and discussed the universality and practicability of deep learning method in automatic building extraction.
Keyword:
Aerial imagery
building extraction
deep learning
hybrid task cascade
instance segmentation
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