基于多源遥感数据的植物物种分类与识别: 研究进展与展望

孔嘉鑫, 张昭臣, 张健

Classification and identification of plant species based on multi-source remote sensing data: Research progress and prospect

Jiaxin Kong, Zhaochen Zhang, Jian Zhang

图2 常用物种分类算法的应用。(a)不同算法分类的物种数; (b)不同算法分类的总体精度。SVM: 支持向量机; RF: 随机森林; MLC: 最大似然分类; DA: 判别式分析; KNN: k-最近邻分类; ANN: 人工神经网络; GLM: 广义线性模型; SAM: 光谱角制图; CART: 分类和回归树; Bayes: 贝叶斯算法; MCS: 多分类系统。括号中N代表每种算法对应的研究案例数。

Fig. 2 Application of commonly used species classification algorithms. (a) The number of species classified by different algorithms; (b) Overall accuracy of different algorithms. SVM, Support Vector Machine; RF, Random Forest; MLC, Maximum Likelihood Classifiers; DA, Discriminant Analysis; KNN, k-Nearest Neighbor; ANN, Artificial Neural Networks; GLM, Generalized Linear Model; SAM, Spectral Angle Mapper; CART, Classification and regression tree; Bayes, Bayesian Classifiers; MCS, Multiple Classifier Systems. “N” in the brackets represents the number of study cases corresponding to each algorithm.