## 2 Datasets and Methods
### Methods
#### 2.3.3 Synergetic Classification  
GF-3 polarization and texture features (8 m) and OHS spectral and index features (10 m) derived from the above steps were used to carry out synergetic classification. Before classification, the spatial resolution of the two kinds of data should be consistent through resampling, which was set to 10 m in this study.  
After ortho-rectification and image coregistration, the above features were classified through three classical supervised classification methods, including maximum likelihood (ML) [25], Mahalanobis distance (MD) [26], and support vector machine (SVM) [21]. In this study, to obtain the fusion datasets of GF-3 PolSAR and OHS hyperspectral data for coastal wetland classification, the layer stacking method was used to combine 11 GF-3-derived polarization and texture features and seven OHS derived spectral and index features into one multiband image at the feature level. This new multiband image includes a total of 18 bands.  
The classifiers represent three different classification principles, as shown below.  
* The ML classifier is one of the most popular methods of classification in remote sensing, in which a pixel with the maximum likelihood is classified into the corresponding class. The likelihood \(L_{k}\) is defined as the posterior probability of a pixel belonging to class \(k\). \[L_{\omega_{i}}=p(\omega_{i}|\mathbf{x})=\frac{p(\mathbf{x}|\omega_{i})p(\omega_{i})}{ p(\mathbf{x})}=\frac{p(\mathbf{x}|\omega_{i})p(\omega_{i})}{\sum\limits_{i=1}^{M}p( \mathbf{x}|\omega_{i})p(\omega_{i})}\] (15) where \(p(\omega_{i})\) and \(p(\mathbf{x}|\omega_{i})\) are the prior probability of class \(\omega_{i}\) and the conditional probability density function to observe \(\mathbf{x}\) from class \(\omega_{i}\), respectively. Usually, \(p(\omega_{i})\) is assumed to be equal, and \(p(\mathbf{x}|\omega_{i})p(\omega_{i})\) is also common to all classes. Therefore, \(L\omega_{i}\) depends on the probability density function \(p(\mathbf{x}|\omega_{i})\).
* The MD classifier is a direction-sensitive distance classifier that uses statistics for each class. It is similar to the ML classifier, but it assumes that all classes have equal covariances, and is, therefore, less time-consuming. The MD of an observation \(x=(x_{1},x_{2},x_{3},\ldots,x_{n})^{T}\) from a set of observations with mean \(\mu=(\mu_{1},\mu_{2},\mu_{3},\ldots,\mu_{n})^{T}\) and covariance matrix \(S\) is defined as [26]: \[D_{M}(x)==\sqrt{(x-\mu)^{T}S^{-1}(x-\mu)}\] (16)  
Figure 7: OHS hyperspectral index features in the YRD. (a) NDVI (b) NDWI.  
* The SVM classifier is a supervised classification method that often yields good classification results from complex and noisy data. It is derived from statistical learning theory that separates the classes with a decision surface that maximizes the margin between the classes. The surface is often called the optimal hyperplane, and the data points closest to the hyperplane are called support vectors. If the training data are linearly separable, any hyperplane can be written as the set of points \(x\) satisfying: \[w^{T}x-b=0\] (17) where \(w\) is the normal vector to the hyperplane.  
The labeled training samples were used as input, and the classification results of seven wetland types were obtained by using the above classifiers to predict the class labels of test images.