更合适的DenseNetBL分类器用于使用无人机基于RGB图像的小型样本树物种分类.
Ni Wang1, Tao Pu1,2, Yali Zhang1
1School of Geographic Information and Tourism, Chuzhou University, Chuzhou, 23900, China.
Heliyon
|October 9, 2023
概括
一种名为DenseNetBL的新方法有效地用有限的数据对树种进行分类. DenseNetBL的性能优于其他模型,特别是在小样本场景中,能够准确地提取树种面积.
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:
- 准确的树种分类对于森林管理和生态研究至关重要.
- 有限的样本大小对开发可靠的分类模型构成重大挑战.
研究的目的:
- 引入和评估DenseNetBL,这是一种使用有限数据集进行树种分类的新方法.
- 评估DenseNetBL在树种面积提取方面的表现.
主要方法:
- 通过将DenseNet架构与瓶层集成,开发了DenseNetBL.
- 在小样本树种数据,遥感数据集上评估了DenseNetBL,并与最先进的分类器进行了比较.
- 通过比较手动和分类器生成的地图的像素面积来量化树种面积.
主要成果:
- 在没有预训练重量的情况下,DenseNetBL的表现优于DenseNet的同行.
- 与Swin变压器和视觉变压器相比,DenseNetBL在小样本分类中表现优异.
- 在小样本分类中,DenseNet33BL获得了最高的准确性 (OA=0.901,Kappa=0.892).
- 结合DenseNet33BL和聚类,提供了最佳的树种面积提取.
结论:
- DenseNetBL是用于小样本树种分类的有效方法.
- 拟议的方法显著提高了树种面积提取精度.
- DenseNetBL为基于遥感的森林库存所面临的挑战提供了一个有前途的解决方案.
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