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

表3 不同分类方法分类精度比较

Table 3 Comparison of classification accuracy of different methods

湿地植被类型 Wetland vegetation type K最近邻 K-nearest neighbor 决策树 Decision tree 随机森林 Random forest 贝叶斯 Bayes U-net深度学习 U-net deep learning 生产者精度 Producer accuracy (%) 用户精度 User accuracy (%) 生产者精度 Producer accuracy (%) 用户精度 User accuracy (%) 生产者精度 Producer accuracy (%) 用户精度 User accuracy (%) 生产者精度 Producer accuracy (%) 用户精度 User accuracy (%) 生产者精度 Producer accuracy (%) 用户精度 User accuracy (%) 短叶茳芏 Cyperus malaccensis 60.00 92.31 80.95 100.00 90.48 100.00 71.43 100.00 75.00 93.75 三棱藨草 Scirpus mariqueter 58.06 58.06 80.00 66.67 86.67 61.90 96.67 72.50 90.32 82.35 厚藤 Ipomoea pescaprae 75.00 100.00 75.00 54.55 81.25 65.00 100.00 72.73 87.50 100.00 芦苇 Phragmites australis 93.30 84.78 87.08 92.39 83.73 96.15 88.04 97.87 98.56 97.17 秋茄 Kandelia candel 37.50 64.29 79.17 67.86 95.83 62.16 95.83 65.71 100.00 100.00 总体精度 Overall accuracy (%) 82.00 84.67 85.33 89.00 95.67 Kappa系数 Kappa coefficient 0.60 0.70 0.73 0.79 0.91