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Expediting Building Footprint Segmentation from High-resolution Remote Sensing Images via progressive lenient supervision
The efficacy of building footprint segmentation from remotely sensed images
has been hindered by model transfer effectiveness. Many existing building
segmentation methods were developed upon the encoder-decoder architecture of
U-Net, in which the encoder is finetuned from the newly developed backbone
networks that are pre-trained on ImageNet. However, the heavy computational
burden of the existing decoder designs hampers the successful transfer of these
modern encoder networks to remote sensing tasks. Even the widely-adopted deep
supervision strategy fails to mitigate these challenges due to its invalid loss
in hybrid regions where foreground and background pixels are intermixed. In
this paper, we conduct a comprehensive evaluation of existing decoder network
designs for building footprint segmentation and propose an efficient framework
denoted as BFSeg to enhance learning efficiency and effectiveness.
Specifically, a densely-connected coarse-to-fine feature fusion decoder network
that facilitates easy and fast feature fusion across scales is proposed.
Moreover, considering the invalidity of hybrid regions in the down-sampled
ground truth during the deep supervision process, we present a lenient deep
supervision and distillation strategy that enables the network to learn proper
knowledge from deep supervision. Building upon these advancements, we have
developed a new family of building segmentation networks, which consistently
surpass prior works with outstanding performance and efficiency across a wide
range of newly developed encoder networks. The code will be released on
https://github.com/HaonanGuo/BFSeg-Efficient-Building-Footprint-Segmentation-Framework.Comment: 13 pages,8 figures. Submitted to IEEE Transactions on Neural Networks
and Learning System
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