Expediting Building Footprint Extraction from High-resolution Remote Sensing Images via progressive lenient supervision

• URL: http://arxiv.org/abs/2307.12220v2
• Date: Wed, 10 Apr 2024 13:15:41 GMT
• Title: Expediting Building Footprint Extraction from High-resolution Remote Sensing Images via progressive lenient supervision
• Authors: Haonan Guo, Bo Du, Chen Wu, Xin Su, Liangpei Zhang,
• Abstract summary: Building footprint segmentation from remotely sensed images has been hindered by model transfer effectiveness. We 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.
• Score: 38.46970858582502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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