基于改进的U-net网络的多源遥感数据的土地覆盖分类研究
Guanjin Zhang1,2, Siti Nur Aliaa Binti Roslan3, Ci Wang4
1Department of Civil Engineering, Faculty of Engineering, University Putra Malaysia, 43400, Serdang, Selangor, Malaysia. gs65671@student.upm.edu.my.
Scientific reports
|September 28, 2023
概括
这项研究增强了U-Net网络,用于使用组合光学和SAR遥感图像进行土地覆盖分类. 改进的方法提高了分类准确性和平均交叉点在欧盟 (mIoU) 上,以更好地监控地面信息.
科学领域:
- 遥感 遥感 遥感 遥感
- 地理空间分析是什么
- 计算机视觉 计算机视觉
背景情况:
- 遥感图像 (光学,SAR) 对于土地覆盖的分类至关重要.
- 单一来源的数据往往限制了分类准确性.
- 虽然U-Net网络很常见,但在准确性和参数效率方面存在局限性.
研究的目的:
- 通过结合光学和SAR数据来提高土地覆盖分类的准确性.
- 增强U-Net网络架构,以更好地提取功能和减少参数.
- 解决传统U-Net在分类小面积地形和整体准确性方面的局限性.
主要方法:
- 为土地覆盖分类开发了一种改进的U-Net架构.
- 增强网络集成了光学和SAR图像频段.
- 修改包括一个卷积块注意力机制,步进-2卷积而不是聚合,以及泄漏ReLU激活.
主要成果:
- 提出的方法实现了0.8905 (光学),0.8609 (SAR) 和0.908 (组合) 的分类精度.
- 在欧盟范围内 (mIoU) 的平均交叉值为0.8104,0.7804和0.8667对于各自的数据集.
- 增强的U-Net表现出比传统的U-Net更好的性能.
结论:
- 将光学和SAR数据与增强的U-Net相结合,显著改善了土地覆盖的分类.
- 建筑修改有效地捕捉空间和通道特征,同时降低网络复杂性.
- 这种方法为使用遥感数据进行准确的地面信息监控提供了更强大的解决方案.
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