深度转移学习的优化方法,用于在干燥热谷的干燥热谷下雨季和干旱季差异下的植被细分
Yayong Chen1,2, Beibei Zhou3, Dapeng Ye1,2
1College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350012, China.
Plants (Basel, Switzerland)
|October 14, 2023
概括
一种新的最大转移潜力指数 (MTPI) 方法优化了植被细分的深度学习转移学习. 它大大减少了数据和训练时间,同时保持了高精度,使模型更高效.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 遥感 遥感 遥感 遥感
背景情况:
- 深度学习模型需要大量的数据和时间进行再培训.
- 转移学习提供了一个解决方案,但缺乏系统的控制.
- 有效的植被细分对于环境监测至关重要.
研究的目的:
- 为深度网络提出一个系统和可控的转移学习方法.
- 为了确定转移学习的最佳数据和特征量.
- 为了提高植被细分任务的效率.
主要方法:
- 开发了最大转移潜力指数 (MTPI) 方法.
- 在雨季植被数据上评估了四个预训练深度网络 (Seg-Net,FCN,MobileNet v2,Res-Net 50).
- 分析了Res-Net 50的层级转移潜力指数 (TPI) 性能.
主要成果:
- 在植被细分方面,Res-Net 50获得了最高的准确性 (93.58%) 和WIoU (88.14%).
- 该MTPI方法确定1000-TDS和37-TP作为速度和低错误风险的最佳条件.
- MTPI转移学习的结果是91.56%的准确度和84.86%的WIoU,数据和代减少了90%.
结论:
- MTPI方法为深度网络转移学习提供了系统性和可控性.
- 优化转移学习显著减少了用于植被细分的计算资源.
- 这种方法对于涉及复杂场景细分的深度学习应用非常有价值.
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