基于高分辨率遥感图像和深度学习模型的土地使用分类
Mengmeng Hao1,2, Xiaohan Dong1,2, Dong Jiang1,2
1Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
PloS one
|April 18, 2024
概括
在高分辨率的土地利用地图中,Swin-UNet显著优于其他深度学习模型,达到96.01%的准确性. 这项研究为远程传感和城市规划应用中选择模型提供了有价值的比较.
科学领域:
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
- 地理空间分析的研究.
背景情况:
- 深度学习模型对于使用高分辨率图像绘制土地利用地图至关重要.
- 已经出现了几种新的深度学习网络建模方法,但它们的比较性能尚不清楚.
研究的目的:
- 系统地比较四个已建立的深度学习模型 (FCN-8s,SegNet,U-Net和Swin-UNet) 的土地利用映射性能.
- 通过联合和F1分数的交集来评估模型概括能力.
主要方法:
- 将FCN-8s,SegNet,U-Net和Swin-UNet模型应用于一个开放的基准高分辨率遥感数据集.
- 对每个模型的整体准确性,结合的交叉点和F1得分的定量评估.
主要成果:
- 斯温-UNet实现了最高的整体准确性 (96.01%),其次是U-Net (91.90%),SegNet (89.86%) 和FCN-8s (80.73%).
- 与其他基于工会和F1得分指标交叉的模型相比,Swin-UNet表现出优越的稳定性和概括能力.
结论:
- 在测试中,Swin-UNet是最有效的深度学习模型,用于高分辨率的土地利用映射.
- 该研究为土地利用地图,城市功能区域识别和自然资源管理中的模型选择提供了关键的参考.
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