多尺度注意网络用于从高分辨率遥感图像中提取建筑物
Jing Chang1, Xiaohui He2,3, Panle Li2
1School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
这项研究引入了一种新的多尺度注意网络 (MSANet),用于精确地从遥感图像中提取建筑物. MSANet增强了特征提取和融合,大大提高了城市规划和资源管理应用程序的准确性.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 从高分辨率遥感图像中提取建筑物对于城市规划,资源管理和环境保护至关重要.
- 深度神经网络 (DNN) 是有前途的,但往往忽视空间信息,并在特征融合过程中遭受噪音放大.
研究的目的:
- 开发一种先进的深度学习模型,即多尺度注意网络 (MSANet),以克服当前基于DNN的建筑提取方法的局限性.
- 通过有效利用多尺度空间和通道信息来提高建筑提取的准确性和稳定性.
主要方法:
- 实施了多尺度注意网络 (MSANet),其中包含多尺度通道和空间注意机制,用于特征提取.
- 采用了适应性层次权重和一个门机制,以实现有效的多尺度特征融合.
- 在WHU的卫星和卫星图像数据集上评估了MSANet.
主要成果:
- 在这两个数据集上,MSANet实现了高性能指标.
- 在WHU空中图像数据集上获得了93.76%的F1得分和88.25%的IOU.
- 在WHU卫星数据集II上获得了77.64%的F1分数和63.46%的IOU,超过了DeepLabV3和GSMC.
结论:
- 拟议的MSANet有效地解决了建筑提取现有的DNN模型的局限性.
- MSANet在从高分辨率遥感图像中提取建筑特征方面表现出卓越的性能,为各种应用提供了有价值的工具.
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