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PolyBuilding: Polygon transformer for building extraction
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AI
DOI：10.1016/j.isprsjprs.2023.03.021.png)
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Abstract
En 中文
We present PolyBuilding, a polygon Transformer for building extraction. PolyBuilding direct predicts vector representation of buildings from remote sensing images. It builds upon an encoder-decoder transformer architecture and simultaneously predicts the bounding boxes and polygons for the building instances. Given a set of polygon queries, the model learns the relations among them and encodes context information from the image to predict the final set of building polygons with a fixed vertex number. Considering that predicting a fixed number of vertices would cause vertex redundancy and reduce polygon regularity, we design a corner classification head to distinguish the building corners. By taking advantage of the corner classification scores, a polygon refinement scheme is designed to remove the redundant vertices and produce the final polygons with regular contours and low complexity. In addition, although the PolyBuilding model is fully end-to -end trainable, we propose a two-phase training strategy to decompose the coordinate regression and corner classification into two stages to alleviate the difficulty of multi-task learning. Comprehensive experiments are conducted on the CrowdAI dataset and Inria dataset. PolyBuilding achieves a new state-of-the-art in terms of pixel-level coverage, instance-level detection performance, and geometry-level properties. Quantitative and qualitative results verify the superiority and effectiveness of our model.
Keywords:
Building extraction
Polygon transformer
Polygon refinement scheme
Two-phase training strategy
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