通过渐进式宽松监督加速高分辨率遥感图像建筑足迹提取
Expediting Building Footprint Extraction from High-resolution Remote Sensing Images via progressive lenient supervision
AI总结：
针对高分辨率遥感建筑足迹分割中解码器计算重、深度监督在混合像素区域失效的问题，提出 BFSeg 框架，结合由粗到细融合解码器与宽松深度监督蒸馏，实现更优性能与效率。
摘要：
从遥感图像中分割建筑足迹的效果一直受到模型迁移有效性的制约。许多现有建筑分割方法是在 U-Net 的编码器-解码器架构上开发的，其中编码器由在 ImageNet 上预训练的最新骨干网络微调得到。然而，现有解码器设计沉重的计算负担阻碍了这些现代编码器网络向遥感任务的成功迁移。即使是被广泛采用的深度监督策略，也因前景和背景像素混合区域中的损失无效而无法缓解这些挑战。本文全面评估了用于建筑足迹分割的现有解码器网络设计，并提出了一个名为 BFSeg 的高效框架，以提升学习效率和效果。具体而言，提出了一种密集连接的由粗到细特征融合解码器网络，可实现跨尺度轻松、快速的特征融合。此外，考虑到深度监督过程中下采样真值在混合区域中的无效性，提出了一种宽松深度监督与蒸馏策略，使网络能够从深度监督中学习恰当的知识。基于这些进展，开发了一系列新的建筑分割网络，在多种最新编码器网络上均以优异的性能和效率持续超越先前工作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
