# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion
### Methodology Analysis
#### 3.5.1 ML Classifier Categories
As illustrated in Figure 15b, the most common form of ML, deep or not, is supervised learning. According to Figure 16, among these algorithms, CNN as a DL model, and RF as a ML model have drawn attention to wetland mapping. As far as the accuracy of algorithms is concerned, DL models are usually the best to use. However, recent studies on challenging classification tasks using multi-source and multi-temporal RS data have pointed to the superiority of the RF approach. RF, as a tree-based ensemble method, has shown superior performance among other ML techniques and has become an often-used method in wetland identification and mapping. RF has several advantages, such as the ability to handle high-dimensional data while being less affected by noise, incorporating continuous and categorical data, and measuring descriptive variable importance [139]. Although many studies indicate that RF produces higher classification accuracy than traditional techniques, it is not usually the most accurate ML method (the reader is referred to [21] and Section 3.7. in this paper). The ensemble methods combine several base estimators' predictions, improving a single estimator's performance and accuracy [139]. In a recent study, a new ensemble-based model, called the multi-grained cascade forest (gcForest), was proposed by [PERSON] [140]. In this approach, several base estimators are stacked to form a layer-by-layer model. As the name implies, a multi-grained scanning technique is used to construct this estimator, and a cascade forest is included to enhance the depth and diversity of the traditional forest model. Shallow structure models combine spectral, structural, and semantic features for the classification of remotely sensed imagery [141]. However, the hetero-spectrum phenomenon in high-resolution RS image classification is very obvious. Therefore, in complex structures, such as heterogeneous wetlands, shallow structure models may be negatively affected by computing power and unable to learn the sample information adequately. The selection of ML algorithms in RS applications such as wetland studies is subjected to several influential factors, such as sensor type and its spatial and spectral resolution, training sample size, and the complexity of the classification problem, etc. For instance, the RF model could be considered to be the most efficient if useful features can be derived from the RS imagery and fed to it. In the case that extracted features are not beneficial in classification, DL models could be the best selection as they have the ability to retrieve complex patterns and informative features from the satellite image data. For example, CNN has shown performance improvements over SVM and RF [21; 142]. However, the main problem with DL approaches is the lack of interpretability due to their hidden layers, \"black box\" nature [143]. Additionally, the efficiency of DL models strongly depends on the availability of a high number of training samples, i.e., ground truth data [21]. Moreover, DL models require specialized knowledge, are expensive to implement computationally, and require dedicated hardware to operate. Overall, the direct comparisons of different kinds of methodologies might not be fair since the experiment settings, and the hyperparameter tuning process for different ML and DL models are not the same (e.g., the input features used for RF and other DL methods are different). With the advent of big data and the continuous addition of satellite-based data, we cannot rely on only one specific methodology for all RS applications. There is a need to develop ensemble and hybrid approaches to tackle this voluminous data coming from a variety of sources. As such, giving merit to a single approach is difficult, as past comparison-based studies and some review papers provide readers with often contradictory conclusions based on processing workload, input datasets, and the evaluated performance metrics, which is somewhat confusing.