基于无人机影像与面向对象-深度学习的滨海湿地植物物种分类

黄雨菲, 路春燕, 贾明明, 王自立, 苏越, 苏艳琳

Plant species classification of coastal wetlands based on UAV images and object- oriented deep learning

Yufei Huang, Chunyan Lu, Mingming Jia, Zili Wang, Yue Su, Yanlin Su

图6 面向对象-U-net深度学习(a)与U-net深度学习(b)方法分类结果局部细节对比。群落交界处各植物种类混生, 边界不清晰, 特征相似, 基于像素分类方法的结果“椒盐”现象明显, 易出现错分问题, 而面向对象分类方法可有效避免该问题, 能更为准确地区分不同植物种类。

Fig. 6 Local detail comparison of classification results between object-oriented U-net deep learning (a) and U-net deep learning (b). At the community boundary, plant species are often mixed with unclear outline and similar image characteristics. The results based on pixel classification method show obvious “pepper and salt” phenomenon, which is prone to misclassification. In contrast, object-oriented method could effectively avoid this problem and distinguish different plant species more accurately.