Biodiv Sci ›› 2019, Vol. 27 ›› Issue (7): 796-812. DOI: 10.17520/biods.2019197
• Review • Previous Articles Next Articles
Jiaxin Kong1,2, Zhaochen Zhang1,2,*(), Jian Zhang1,2
Received:
2019-06-17
Accepted:
2019-07-11
Online:
2019-07-20
Published:
2019-08-21
Contact:
Zhaochen Zhang
Jiaxin Kong, Zhaochen Zhang, Jian Zhang. Classification and identification of plant species based on multi-source remote sensing data: Research progress and prospect[J]. Biodiv Sci, 2019, 27(7): 796-812.
Fig. 1 The papers on plant species classification and identification using different types of remote sensing data published between 2000 and 2018 in three top journals in remote sensing, including International Journal of Remote Sensing, Remote Sensing of Environment, and Remote Sensing.
研究对象 Research subjects | 相关案例数量 Number of case studies | 案例 Selected case studies |
---|---|---|
热带/亚热带森林 Tropical/subtropical forests | 10 | |
热带稀树草原 Savanna | 8 | |
温带森林 Temperate forests | 56 | |
北方森林/针叶林 Boreal forest or Coniferous forest | 19 | |
红树林 Mangrove | 7 | |
草地 Grassland | 4 | |
城市树种 Urban tree species | 11 | |
入侵植物 Invasive plants | 5 |
Table 1 Statistics of 120 selected study cases of plant species identification based on remote sensing data
研究对象 Research subjects | 相关案例数量 Number of case studies | 案例 Selected case studies |
---|---|---|
热带/亚热带森林 Tropical/subtropical forests | 10 | |
热带稀树草原 Savanna | 8 | |
温带森林 Temperate forests | 56 | |
北方森林/针叶林 Boreal forest or Coniferous forest | 19 | |
红树林 Mangrove | 7 | |
草地 Grassland | 4 | |
城市树种 Urban tree species | 11 | |
入侵植物 Invasive plants | 5 |
分类算法 Species classification algorithms | 优缺点 Advantages and disadvantages | 案例列举 Case studies | ||||
---|---|---|---|---|---|---|
输入数据是否需要分布假设 Does the input data need specific distribution assumption? | 是否需要训练数据 Is the training data needed? | 对各种分类问题的适用性 Applicability to different kinds of species classifications | 算法复杂性和计算成本 Algorithmic complexity and computational cost | 可操作性 Is it easy to use for ecologists? | ||
支持向量机 Support Vector Machine (SVM) | 否 No | 较少 Less | 较高 High | 较高 High | 容易 Easy | |
随机森林 Random Forest (RF) | 否 No | 较少 Less | 较高 High | 较低 Low | 容易 Easy | |
最大似然分类 Maximum Likelihood Classifiers (MLC) | 是 Yes | 较多 More | 较高 High | 较高 High | 容易 Easy | |
判别式分析 Discriminant Analysis (DA) | 是 Yes | 较多 More | 较高 High | 较低 Low | 容易 Easy | |
k-最近邻分类 k-Nearest Neighbor (KNN) | 否 No | 否 No | 较高 High | 较高 High | 容易 Easy | |
人工神经网络 Artificial Neural Networks (ANN) | 否 No | 较多 More | 较高 High | 较高 High | 较难 Difficult | |
光谱角制图 Spectral Angle Mapper (SAM) | 否 No | 否 No | 较低 Low | 较高 High | 容易 Easy | |
广义线性模型 Generalized Linear Model (GLM) | 是 Yes | 否 No | 较低 Low | 较高 High | 较难 Difficult | |
分类和回归树 Classification and regression tree (CART) | 否 No | 较少 Less | 较高 High | 较低 Low | 容易 Easy | |
贝叶斯分类算法 Bayesian Classifiers | 否 No | 否 No | 较高 High | 较低 Low | 容易 Easy |
Table 2 Advantages and disadvantages of commonly used species classification algorithms and case studies
分类算法 Species classification algorithms | 优缺点 Advantages and disadvantages | 案例列举 Case studies | ||||
---|---|---|---|---|---|---|
输入数据是否需要分布假设 Does the input data need specific distribution assumption? | 是否需要训练数据 Is the training data needed? | 对各种分类问题的适用性 Applicability to different kinds of species classifications | 算法复杂性和计算成本 Algorithmic complexity and computational cost | 可操作性 Is it easy to use for ecologists? | ||
支持向量机 Support Vector Machine (SVM) | 否 No | 较少 Less | 较高 High | 较高 High | 容易 Easy | |
随机森林 Random Forest (RF) | 否 No | 较少 Less | 较高 High | 较低 Low | 容易 Easy | |
最大似然分类 Maximum Likelihood Classifiers (MLC) | 是 Yes | 较多 More | 较高 High | 较高 High | 容易 Easy | |
判别式分析 Discriminant Analysis (DA) | 是 Yes | 较多 More | 较高 High | 较低 Low | 容易 Easy | |
k-最近邻分类 k-Nearest Neighbor (KNN) | 否 No | 否 No | 较高 High | 较高 High | 容易 Easy | |
人工神经网络 Artificial Neural Networks (ANN) | 否 No | 较多 More | 较高 High | 较高 High | 较难 Difficult | |
光谱角制图 Spectral Angle Mapper (SAM) | 否 No | 否 No | 较低 Low | 较高 High | 容易 Easy | |
广义线性模型 Generalized Linear Model (GLM) | 是 Yes | 否 No | 较低 Low | 较高 High | 较难 Difficult | |
分类和回归树 Classification and regression tree (CART) | 否 No | 较少 Less | 较高 High | 较低 Low | 容易 Easy | |
贝叶斯分类算法 Bayesian Classifiers | 否 No | 否 No | 较高 High | 较低 Low | 容易 Easy |
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.
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