# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion
### Methodology Analysis  
#### 3.5.1 ML Classifier Categories  
In the aftermath of its introduction, land cover mapping algorithms have been refined and upgraded many times [134]. For instance, when it comes to water extraction, the procedure can be done considering three stages: visual interpretation, semi-automatic interpretation, and automatic interpretation [135]. In recent years, ML has emerged as the most common wetland extraction method for automatic interpretation, and supervised and unsupervised ML techniques for RS image classification have progressed greatly. The challenges and difficulty of discrimination among classes, however, continue to affect the accuracy of surface mapping and data extraction. Therefore, classification is still a difficult task due to confusion with different cover classes. Various classification techniques have been used to deal with land cover classification and improve classification accuracy. At present, the use of shallow ML algorithms, such as support vector machine (SVM), backpropagation (BP) neural networks, decision tree (DT),  
Figure 14: Frequency of single- and multi-date RS data usage in wetland studies. Figure 13: Frequency of high-resolution, medium-resolution, and coarse-resolution images used in wetland studies. random forest (RF), extreme gradient boosting (XGBoost), and K-Nearest Neighbors (KNN), have been widely applied to RS data in extracting wetland information and improving the accuracy of wetland classification and CD. These approaches are able to handle complex nonlinear relationships without taking into account the statistical assumptions. However, as a diverse type of land cover, wetlands involve a variety of spatial, temporal, and spectral characteristics, and no single algorithm has proven to meet all the needs of wetland monitoring and be optimal across all use cases [76]. The shallow ML algorithms work well in extracting information from wetland areas; however, they require a lot of prior knowledge to be trained and high learning costs to migrate models. Since its inception, DL, a subfield of ML, is emerging as a powerful tool to address problems by learning from data. DL is a milestone data representation learning technology that has taken the world by storm due to its success. As a way to automate predictive analytics, DL allows trainable models composed of sequence of processing layers and nonlinear mappings to learn representations of data with multiple levels of abstraction [136]. A DL network has a strong function expression ability, can learn more complex training samples, and has good robustness for the classification of complex features such as wetland landscapes in RS images [21; 77; 137]. CNN is a breakthrough technique in DL, and it is a kind of feed-forward neural network, which has good performance in image processing and data mining. The artificial neurons in CNN models can respond to a part of the surrounding units in the coverage area. By the inclusion of several building blocks, such as convolutional layers and pooling layers in its structure, a CNN framework is capable of better extracting medium and high-level abstractions from the original images. Some scholars have applied it in wetland classification studies and have reported higher overall accuracy values than those obtained using shallow ML [21; 77; 96; 138]. ML algorithms' robustness and generalization capability have made them the major classification methods. Various ML and classification techniques have been used successfully and widely in wetland studies. Considering the structure of different methodologies employed during the last three decades, the classification algorithms were grouped into eight major categories in this paper: (1) ensemble learning; (2) decision tree; (3) Kernel-based; (4) distance-based; (5) DL-based; (6) ANN; (7) instance-based; (8) Bayesian (see Figures 15a and 16). While there is an overlap between the ANN and DL categories, the former includes shallow, two- or three-layer networks, whereas the latter includes more complex and deeper networks.