Moghalles Khaled, Al-Huda Zaid, Al-Alimi Dalal, Gu Yeong Hyeon, Al-Antari Mugahed A
School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China.
Stirling College, Chengdu University, Chengdu, China.
Sci Rep. 2025 Aug 13;15(1):29712. doi: 10.1038/s41598-025-13890-8.
The automated segmentation of buildings from remotely sensed imagery has undergone extensive research and application across various industrial domains. Despite this, several challenges persist, including incomplete internal extraction, low accuracy in edge segmentation, and difficulties in predicting irregular targets. We have introduced a novel approach to address these issues: an end-to-end residual U-structure embedded within a U-Net, enhanced by a frequency attention module and a hybrid loss function. The novel residual U-structure is introduced to replace the encode-decode blocks of traditional U-Nets, and the hybrid loss function is utilized to guide segmentation for more complete and accurate segmentation masks. A frequency attention module is also implemented to emphasize essential features and minimize irrelevant ones. A comparison of the proposed framework with other baseline schemes was conducted on four benchmark data sets, and the experimental results demonstrate that our framework performs better segmentation than other baseline state-of-the-art schemes.
从遥感图像中自动分割建筑物已经在各个工业领域进行了广泛的研究和应用。尽管如此，仍然存在一些挑战，包括内部提取不完整、边缘分割精度低以及预测不规则目标困难。我们引入了一种新颖的方法来解决这些问题：一种嵌入在U-Net中的端到端残差U结构，通过频率注意力模块和混合损失函数进行增强。引入新颖的残差U结构来替代传统U-Net的编码-解码块，并利用混合损失函数来指导分割，以获得更完整和准确的分割掩码。还实现了一个频率注意力模块，以强调基本特征并最小化无关特征。在四个基准数据集上对所提出的框架与其他基线方案进行了比较，实验结果表明，我们的框架比其他基线最先进方案具有更好的分割性能。
