Li Yingjian, Li Yonggang, Zhu Xiangbin, Fang Haojie, Ye Lihua
School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China.
College of Information Science and Engineering, Jiaxing University, Jiaxing, 314001, China.
Sci Rep. 2024 Aug 17;14(1):19067. doi: 10.1038/s41598-024-70019-z.
Building extraction aims to extract building pixels from remote sensing imagery, which plays a significant role in urban planning, dynamic urban monitoring, and many other applications. UNet3+ is widely applied in building extraction from remote sensing images. However, it still faces issues such as low segmentation accuracy, imprecise boundary delineation, and the complexity of network models. Therefore, based on the UNet3+ model, this paper proposes a 3D Joint Attention (3DJA) module that effectively enhances the correlation between local and global features, obtaining more accurate object semantic information and enhancing feature representation. The 3DJA module models semantic interdependence in the vertical and horizontal dimensions to obtain feature map spatial encoding information, as well as in the channel dimensions to increase the correlation between dependent channel graphs. In addition, a bottleneck module is constructed to reduce the number of network parameters and improve model training efficiency. Many experiments are conducted on publicly accessible WHU,INRIA and Massachusetts building dataset, and the benchmarks, BOMSC-Net, CVNet, SCA-Net, SPCL-Net, ACMFNet, MFCF-Net models are selected for comparison with the 3DJA-UNet3+ model proposed in this paper. The experimental results show that 3DJA-UNet3+ achieves competitive results in three evaluation indicators: overall accuracy, mean intersection over union, and F1-score. The code will be available at https://github.com/EnjiLi/3DJA-UNet3Plus .
建筑物提取旨在从遥感影像中提取建筑物像素，这在城市规划、城市动态监测及许多其他应用中发挥着重要作用。UNet3+被广泛应用于遥感影像的建筑物提取。然而，它仍然面临着诸如分割精度低、边界划定不精确以及网络模型复杂等问题。因此，本文基于UNet3+模型，提出了一种3D联合注意力（3DJA）模块，该模块有效地增强了局部和全局特征之间的相关性，获得了更准确的目标语义信息并增强了特征表示。3DJA模块在垂直和水平维度上对语义相互依赖性进行建模，以获得特征图空间编码信息，同时在通道维度上增加相关通道图之间的相关性。此外，构建了一个瓶颈模块以减少网络参数数量并提高模型训练效率。在可公开获取的WHU、INRIA和马萨诸塞州建筑物数据集上进行了许多实验，并选择了基准模型BOMSC-Net、CVNet、SCA-Net、SPCL-Net、ACMFNet、MFCF-Net与本文提出的3DJA-UNet3+模型进行比较。实验结果表明，3DJA-UNet3+在总体精度、平均交并比和F1分数这三个评估指标上取得了具有竞争力的结果。代码将在https://github.com/EnjiLi/3DJA-UNet3Plus上提供。
