## 6 Wetland Vegetation Classification
### Method
After image segmentation we conducted a supervised classification with a nearest neighbor classifier, which used a set of training samples from different classes to assign membership values. We used 74 sample sites for training (24 for forest, 23 for scrub, 18 for prairie, and nine for water). The membership values were between 0 and 1, depending on the image object's feature space distance from its nearest neighbor. A membership value of 1 is assigned when the image object is identical to a sample. If the image object differs from the sample, the feature space distance has a fuzzy dependency on the feature space distance from the nearest sample of a class. The user can select the features to be considered for the feature space. For an image object to be classified, only the nearest sample is used to evaluate its membership value. The effective membership function at each point in the feature space is a combination of the fuzzy function over all samples of that class. When the membership function is described as one dimensional it means it is related to one feature [67]. We applied five classification scenarios, which adopted different nearest neighbor feature spaces as described in Table 3. Scenario 1 used only optical image information with the five multispectral bands of RapidEye (blue, green, and red visible, red-edge, and near-infrared) and its normalized difference vegetation index (NDVI), based on the segmentation type O. Scenario 2 used all available features from TSX and RapidEye image layers, with the segmentation type O. Scenario 3 adopted five TSX decomposition components layers (single, double, double from co-pol, double from cross-pol, and volume), but was based on the segmentation type O, in which the image objects were created with RapidEye's spectral features. Since the differentiation of vegetation type into forest, scrub, and prairie using optical satellite imagery is relatively easy, we assumed that image objects by segmentation type O would be reliable as homogeneous vegetation units. Hence, we could examine how the SAR polarimetric decomposition components were related to vegetation types by comparing these three combinations of input layers. Scenario 4 used the five TSX decomposition components layers based on the segmentation type S, in which the image objects were created with TSX data only. This scenario provided the contrasting case of using only SAR features. Finally, Scenario 5 adopted four TSX decomposition results using [PERSON]'s method with segmentation type O, which we then compared with results from Scenario 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **Image Layer Features Used for Classification (Mean Object Value)** & **Segmentation Type** \\ \hline Scenario 1 & RapidEye’s blue, green, red, red-edge, NIR, and NDVI & Type O \\ \hline Scenario 2 & RapidEye’s blue, green, red, red-edge, NIR and NDVI \& TerraSAR-X’s single, \\  & double, double from co-pol, double from cross-pol, and volume & Type O \\ \hline Scenario 3 & TerraSAR-X’s single, double, double from co-pol, double from cross-pol, and volume & Type O \\ \hline Scenario 4 & TerraSAR-X’s single, double, double from co-pol, double from cross-pol, and volume & Type S \\ \hline Scenario 5 & TerraSAR-X’s single, double, volume and helix using the Yamaguchi decomposition & Type O \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification scenarios.