消费级无人机图像促进了物种丰富的萨凡纳树层的语义细分
Manuel R Popp1, Jesse M Kalwij2,3,4
1Institute of Geography and Geoecology, Karlsruhe Institute of Technology, Reinhard-Baumeister-Platz 1, 76131, Karlsruhe, Germany.
Scientific reports
|August 24, 2023
概括
卷积神经网络 (CNN) 可以使用无人机图像绘制萨凡纳树种的地图. 虽然在不同距离上有效,但由于工厂的变化,模型的准确性随着时间的推移而下降,这凸显了对多样化的数据集的需求.
科学领域:
- 生态生态学 生态生态学
- 遥感 遥感 遥感 遥感
- 计算机科学 计算机科学
背景情况:
- 传统的森林库存是劳动密集型的,限制了监测范围和频率.
- 遥感提供了经济高效的大规模监控解决方案.
- 卷积神经网络 (CNN) 显示出使用无人机图像对植被进行分类的前景.
研究的目的:
- 评估使用CNN用于萨凡纳生态系统中的树种语义细分的可行性.
- 为了确定小数据集的高分辨率图像是否足以训练CNN模型.
- 评估训练有素的CNN模型的空间和时间可转移性.
主要方法:
- 在一个18公的沙漠训练区,在高分辨率的RGB光谱上训练了三个CNN模型 (U-Net,FC-DenseNet,DeepLabv3+).
- 在相邻和遥远地区测试模型性能.
- 评估模型的准确性随着时间的推移而变化,以解释植物形态学变化.
主要成果:
- 美国有线电视新闻网 (CNN) 的模型成功地对草原树种进行了语义细分.
- 模型证明了跨空间的可转移性,识别了邻近 (F1=0.68) 和遥远 (F1=0.61) 区域的树木.
- 随着时间的推移,由于植物形态的变化,模型的准确性下降.
结论:
- 基于CNN的树木绘图使用消费级无人机图像在草原生态系统中是可行的.
- 模型的稳定性可以通过更大,更异质的数据集来提高,以捕捉植物形态的时空变化.
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