# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion
### Feature Selection and Derivation  
The features used in wetlands research from optical data are mostly spectral reflectance characteristics, such as the normal difference vegetation index (NDVI), normal difference water index (NDWI), and soil-adjusted vegetation index (SAVI), etc. These feature indices have been widely used in monitoring and mapping wetlands [7; 46; 76; 99]. When identifying wetlands, relations to neighbor objects may be useful features because of the spatial relationship of objects in distribution. These features aid the identification of wetlands and improve the accuracy of classification. For instance, texture consists of visual patterns or spatial patterns of pixels that may have statistical properties, structural properties, or both [172]. The texture of imagery has been extensively investigated and studied as a supplement to spectral data for the analysis of wetland zones, and has proven effective [172; 173]. There are many ways to describe texture. The gray-level covariance co-occurrence matrix (GLCM) has proven effective for the identification of wetlands [174].  
In the current meta-analysis, different feature sets employed in wetland studies are grouped as follows: (1) original spectral bands or backscattering coefficients; (2) spectral indices; (3) image texture components derived from the GLCM; (4) SAR polarimetric features (5) geometric features; and (6) topographic variables derived from digital terrain models (see Figure 18 and Table 8).  
Rather than relying solely on a single feature category, the majority of wetland studies combine several features from multiple categories. Spectral indices and SAR polarimetric decompositions, followed by textural, topographical, and geometric features, are frequently used to determine the contribution of each feature category. According to several studies involving the feature selection process, the spectral and polarimetric features that are simple and easy to extract have been shown to be more powerful than other types of features in wetland studies and in discriminating class types.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**\#** & **Ref.** & **Application Type** & **Publication Date** & **\#** & **Ref.** & **Application Type** & **Publication Date** \\ \hline
**13** & [96] & Classification & Jan, 2020 & 33 & [162] & Classification & Nov, 2021 \\ \hline
**14** & [163] &
\begin{tabular}{c} Inundation analysis and Biomass \\ \end{tabular} & Feb, 2020 & 34 & [134] & Classification & Dec, 2021 \\ \hline
**15** & [164] & Classification & Mar, 2020 & 35 & [97] &
\begin{tabular}{c} Wetland \\ Extent Extraction \\ \end{tabular} & Jan, 2022 \\ \hline
**16** & [51] &
\begin{tabular}{c} Wetland \\ vegetation mapping \\ \end{tabular} & Apr, 2020 & 36 & [94] & Classification & Jan, 2022 \\ \hline
**17** & [165] & Classification & Apr, 2020 & 37 & [166] & Classification & Feb, 2022 \\ \hline
**18** & [99] & Classification & May, 2020 & 38 & [167] &
\begin{tabular}{c} Wetland \\ Inundation Analysis \\ \end{tabular} & May, 2022 \\ \hline
**19** & [168] &
\begin{tabular}{c} Water Cover \\ Detection \\ \end{tabular} & May, 2020 & 39 & [169] & Classification & May, 2022 \\ \hline
**20** & [170] & Classification & May, 2020 & 40 & [171] &
\begin{tabular}{c} Change \\ Detection \\ \end{tabular} & Jun, 2022 \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._