Robust Extraction of Vectorized Buildings via Bidirectional Tracing of Keypoints From Remotely Sensed Imagery
Zhen Shu, Xiangyun Hu, Hengming Dai, Lunhao Duan, Zhili Zhang, Lin Zhang, Litong Zhang · IEEE Transactions on Geoscience and Remote Sensing · 2023
Automatic extraction of vector polygons of buildings from remotely sensed images is an important but difficult task. Recent existing methods based on deep learning usually adopt a multi-stage solution of semantic segmentation, contour detection, and polygon simplification. Such a long processing chain may lead to unreliable results as the boundary regularization and optimization processes are ultimately completed by utilizing low-level features, which ignores the potential of deep features in polygon generation. In this paper, we present an algorithm for directly extracting simplified polygons of buildings in remotely sensed images. The key of this task is the encoding of the polygon structure. PolyMapper [1] utilizes a recurrent neural network (RNN) to produce vertices of a polygon sequentially. Due to the limitation of RNN, this approach is unstable and difficult to deal with objects with complex shapes. In this work, we encode the polygon into a tensor representation and utilize a non-recurrent manner to recover the polygon structure. In our algorithm, two types of points are utilized, i.e., the corner point and the connecting point. Corner points are utilized to delineate the building outlines and form the vertices of the final polygon. Meanwhile, connecting points are sampled from the edges of the buildings for the assistance of the connection of the corner points. Furthermore, we predict the forward and backward directions of each keypoint in a polygon and propose a bidirectional tracing strategy for the polygon structure recovery. Our approach is simple, effective and robust. Experiments on public datasets demonstrate the superiority of the proposed algorithm. The code is made publicly available at https://github.com/sz94/bldvec.