建筑物提取旨在从遥感图像中提取建筑物像素,在城市规划中发挥着重要作用,城市动态监测,和许多其他应用程序。UNet3+广泛应用于遥感图像中建筑物的提取。然而,它仍然面临着分割精度低、不精确的边界划定,以及网络模型的复杂性。因此,基于UNet3+模型,本文提出了一种3D联合注意(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,获取更准确的对象语义信息,增强特征表示。3DJA模块在纵向和横向维度上对语义相互依赖进行建模,以获得特征图空间编码信息,以及在通道维度中增加依赖通道图之间的相关性。此外,构建瓶颈模块,减少网络参数数量,提高模型训练效率。许多实验是在可公开访问的WHU上进行的,INRIA和马萨诸塞州建筑数据集,和基准,BOMSC-Net,CVNet,SCA-Net,SPCL-Net,ACMFNet,选择MFCF-Net模型与本文提出的3DJA-UNet3模型进行比较。实验结果表明,3DJA-UNet3+在总体准确率、工会的平均交叉点,和F1得分。该代码将在https://github.com/EnjiLi/3DJA-UNet3Plus上提供。
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 .