一种基于3DJA-UNet3的远程传感图像提取建筑物的方法
Yingjian Li1, Yonggang Li2, Xiangbin Zhu1
1School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China.
Scientific reports
|August 17, 2024
概括
本研究引入了一个3D联合注意 (3DJA) 模块,以改进UNet3+用于从遥感图像中提取建筑物,提高准确性和效率. 新模型在基准数据集上取得了竞争性结果.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 从遥感图像中提取建筑物对于城市规划和监测至关重要.
- 现有的UNet3+模型面临着细分精度和边界精度方面的挑战.
研究的目的:
- 为了提高建筑物提取精度和效率,使用一个新的UNet3+模型模块来提高提取精度和效率.
- 改进特征表示,减少网络复杂性.
主要方法:
- 开发了一个3D联合注意 (3DJA) 模块,以改善特征相关性 (本地/全球,通道).
- 一个瓶模块被纳入,以减少参数和提高培训效率.
- 在WHU,INRIA和马萨诸塞州的建筑数据集上进行了实验.
主要成果:
- 拟议的3DJA-UNet3+模型展示了具有竞争力的性能.
- 该模型在整体准确性,平均交叉与联合以及F1分数方面取得了强的结果.
- 3DJA模块有效地增强了语义信息和特征表示.
结论:
- 3DJA-UNet3+模型为从遥感数据中提取建筑物提供了有效的解决方案.
- 拟议的模块可以提高细分精度和模型效率.
- 该方法为城市规划和监测应用提供了宝贵的贡献.
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