## 6 Wetland Vegetation Classification  
### Method  
Quad-pol-based vegetation classification previously has been conducted with two different data types. One type relied on coefficients of radar backscatter [53; 54; 55; 56; 57] and the other type used decomposition maps, which are based on physical scattering mechanisms [23; 27; 29; 30; 31; 34; 46; 47; 48]. In this study, we compared classification results derived from PolSAR decomposition with those derived from optical satellite image data. We also compared classification results using [PERSON]'s decomposition method [32] to evaluate the benefit of our decomposition for wetland vegetation classification. We applied an object-oriented approach to classify vegetation cover. This approach is based on classifying objects or image segments that are delineated as homogeneous units with similar spectral characteristics (this delineation process is called segmentation), rather than classifying individual pixel values. Segmentation enables the acquisition of a variety of textural and spatial features, such as shape, in addition to spectral values, resulting in improved classification accuracy [58]. SAR images usually have speckle noises or relative roughness compared to optical images. Similar artifacts of the SAR signal, which can be found in the scattering component results from TSX quadruple data, prevent the characterization of the surface type into specific classes using only pixel values. Thus, we assumed that the object-oriented classification would be a better approach than a pixel-based classification. The advantages of object-oriented classification using high-resolution image data have been reported by many studies [59; 60; 61; 62; 63]. However, determining appropriate segmentation parameters is a time-consuming process, generally based on trial and error evaluation to derive homogeneous image segments representing similar thematic units such as vegetation types [64; 65; 62]. We applied a multi-resolution segmentation method with eCognition Developer 8 [66]. First, different parameters for the scale factor, shape, and compactness were evaluated through iterative trial and error (Table 2). For the object homogeneity eCognition Developer adopts three criteria of scale, shape/color and compactness/smoothness. The shape defines the percentage the spectral values of the image layers will contribute to the entire homogeneity criterion. As the shape and the color are complementary, the assigned shape value determines automatically the color criteria. In addition to spectral information, the object homogeneity is optimized with regard to the object shape, defined by the compactness parameter. The compactness should be used when different image objects are rather compact, are separated from non-compact objects only by a relatively weak spectral contrast. We then generated three types of segmentation outputs based on the different optical and SAR datasets. One output relied on optical RapidEye's spectral characteristics (seg \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Segmentation Type** & **Image Source** & **Image Layer** & **Scale** & **Shape** & **Compactness** \\ \hline Type O & RapidEye & Blue, Green, Red, Red-Edge, NIR, NDVI & 50 & 0.1 & 0.5 \\ \hline Type S & TSX & Single, Double, Double from co-pol, & & & \\  & & Double from cross-pol, Volume & & & \\ \hline Type M & TSX \& RapidEye & Blue, Green, Red, Red-Edge, NIR, NDVI, & & & \\  & & & Single, Double, Double from co-pol, & & & \\  & & & Double from cross-pol, Volume & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Parameter settings for image segmentation. second output was based on processed SAR decomposition components (segmentation type: S). The third output was calculated with both TSX and RapidEye features (segmentation type: M). We configured image segmentation settings to build object boundaries around sample sites for the training and reference samples. In this way, sample sites were delineated as image objects for use in training and testing of the supervised classification.