用无人机多谱遥感进行作物分类,采用增强的ResNet50残余网络
Chenwei Xu1, Shixian Lu1, Xiang Feng2
1Yunnan Agricultural University, Yunnan Provincial International Joint Research and Development Center for Smart Environment, Yunnan Agricultural University, Kunming, China.
PloS one
|December 30, 2025
概括
这项研究增强了ResNet50模型,用于使用多谱数据进行作物分类. RGB+NIR+Edge带组合和批量规范化层显著提高了烟烟草和玉米识别的准确性.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
背景情况:
- ResNet50提供快速训练和高精度的作物特征提取.
- 多光谱数据的频段相关性和冗余性可能会降低分类准确性.
- 标准ResNet50要求对多频段数据和高精度小规模分类进行改进.
研究的目的:
- 为了确定最佳的光谱带组合来分类烟草和玉米.
- 增强ResNet50模型,以提高作物分类任务的性能.
- 评估建筑修改对分类准确性的影响.
主要方法:
- 通过整合批量规范化 (BN),金字塔聚合和隐藏层来修改ResNet50模型.
- 试验了这些增强的七种不同的组合.
- 使用准确度,精度,回忆,卡帕系数和F1分数等指标评估分类性能.
主要成果:
- 在RGB+NIR+Edge频段组合中,烟烟草和玉米的分类精度达到最高 (94.48%).
- 仅仅引入BN层在测试的策略中提供了最显著的改进.
- 改进后的模型显示了高精度 (94.66%),回忆 (94.48%) 和F1得分 (94.49%).
结论:
- RGB+NIR+Edge频段组合是使用增强的ResNet50.50.进行烟雾固化烟草和玉米分类的最佳选择.
- 批量规范化层对于提高ResNet50在多谱作物分类中的性能至关重要.
- 该研究提供了一种强大的方法,用于使用深度学习模型进行高精度作物分类.
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