## 3 Methods
### Optimal Parameter Selection  
Several key factors affect the final classification performance: feature combination, window size to compute textures and threshold to extract water surface. The steps described in Sections 3.1-3.7 were repeated, and the values of these key factors maximizing overall accuracy were selected.  
Feature Combination: Based on different types of features, including normalized difference spectral indices, backscatter features and image textures derived from spectral indices and SAR images, many kinds of feature combinations can be obtained. To investigate the optimal feature combination, six groups of features (Table 3) were used to obtain six aquaculture maps, respectively. The threshold \(T\) = 0.06 and texture window size with 15 were used. The determinations of \(T\) and window size are described in Sections 4.2 and 4.3, respectively. The 1000 random selected samples were used to evaluate their performances.  
Threshold to Extract Water Surface: Since aquaculture areas are first classified as a part of a water body, the threshold separating water and non-water surfaces plays an important role in the whole process. In this study, water surface was extracted by thresholding an NDWI image, and only one parameter \(T\) was used. The spectral indices, textures from indices and SAR images, as well as ETOPO1, were used. The window size was set at 15 \(\times\) 15 pixels. The thresholds between \(-\)0.2 and 0.2 were tested, with a step of 0.02. The accuracies were evaluated using 1000 samples.  
Window Size to Compute GLCM Features: The window size of gray-level co-occurrence matrix can directly affect the detection of aquaculture in a complex water environment. According to [71], a texture window should cover at least the minimum detectable target. Although inland aquaculture ponds have regular arrangements and clear textures, the ponds can be properly separated by thresholding NDWI image. However, the detection of mariculture facilities mainly depends on image textures. Based on our visual interpretation from Google Earth, the size of minimum detectable mariculture block is about 50 to 150 m. Therefore, a window size of 15 \(\times\) 15 pixels was preferred. However, the experiments using a window of 5 \(\times\) 5 pixels, 15 \(\times\) 15 pixels, and 30 \(\times\) 30 pixels were also carried out to determine the optimal size.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature Group ID**} & \multirow{2}{*}{**ETOPO1**} & \multirow{2}{*}{**Indices**} & \multirow{2}{*}{**SAR**} & \multirow{2}{*}{**Textures of Indices**} & \multirow{2}{*}{**Textures of SAR**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{5-6}  & & & & & & **OA (\%)** & **Kappa** \\ \hline A & \(\surd\) & \(\surd\) & & & & 81.5 & 0.609 \\ B & \(\surd\) & \(\surd\) & \(\surd\) & & & 83.1 & 0.639 \\ C & \(\surd\) & \(\surd\) & & \(\surd\) & & 88.2 & 0.748 \\ D & \(\surd\) & & \(\surd\) & & \(\surd\) & 86.9 & 0.719 \\ E & \(\surd\) & \(\surd\) & & \(\surd\) & \(\surd\) & 89.5 & 0.776 \\ F & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 89.0 & 0.765 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification performances using different feature combinations.  
Figure 6: Overview of the morphological post processes of extracted aquaculture land in this study: (**a**) raw classification results based on RF, (**b**) exclusion of misclassified river, and (**c**) final classification map with postprocessing.