# Optimization of DG-LRG Water Extraction Algorithm Considering Polarization and Texture Information
## 3 Optimized Local Region Growth Algorithm Considering Polarization and Texture Information
### Local Region Growth Criteria Based on Polarization and Texture Information  
#### 3.3.1 Extraction of Texture Features  
The Gray-level co-occurrence matrix (GLCM) is a method for extracting texture features from grayscale images. The co-occurrence matrix is obtained by calculating the grayscale image, following which some feature values of the matrix are obtained by calculating the co-occurrence matrix to represent certain texture features of the image. This method defines a total of 14 texture features [20,21]. Due to the differences in back-scatter information between SAR images with different polarization methods for the same type of ground object, for this experiment, the db values of the GF-3 satellite were used for GLCM texture feature extraction. In our experiment, eight commonly used statistics--the Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Angular Second Moment, and Correlation--were selected to form a feature matrix to characterize the texture of SAR images. Due to slight differences in back-scatter information and different texture features in SAR images with different polarization methods, in order to reduce redundant information and improve efficiency, while also considering the texture differences of polarization methods, principal component analysis was performed on the feature matrices of the two polarization methods and the first three principal components were extracted for the final statistics. Through experiments, three types of statistical measurements were extracted for the HH polarization GF-3 images: Homogeneity, Entropy, and Contrast. Meanwhile, three statistical measurements were also extracted for the HV polarization GF-3 images: Homogeneity, Dissimilarity, and Angular Second Moments. The expressions and descriptions for these feature statistics are provided in the Table 2. In the table, i and j represent the grayscale progression of the image, while Pi,j represents the probability of pixels with grayscale j appearing from pixels with grayscale i in the image. In our experiment, we divided the db values of the SAR images into 64 levels and moved them in four directions: 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\). For texture feature extraction, the selected window was 7 \(\times\) 7.  
\begin{table}
\begin{tabular}{c c c} \hline
**Texture Features** & **Expression** & **Description** \\ \hline Homogeneity & \(\text{Hom}=-\sum\limits_{i}\sum\limits_{j}\dfrac{1}{1+\left(\text{i}-\text{j} \right)^{2}}\text{P}_{\text{ij}}\) & Used to measure the local variation of image texture; water blocks are highly homogeneous areas in the image. \\ Entropy & \(\text{Ent}=-\sum\limits_{i}\sum\limits_{j}\text{P}_{\text{ij}}\times\text{ In}_{\text{ij}}\) & Reflects the rate of change in image grayscale; the entropy value in the center of the water body is small, while the entropy value at the shore is large. \\ Contrast & \(\text{Con}=\sum\limits_{i}\sum\limits_{j}\text{P}_{\text{ij}}\times\left(\text{ i}-\text{j}\right)^{2}\) & Reflects the clarity of the image and the depth of the texture grooves. \\ Dissimilarity & \(\text{Dis}=\sum\limits_{i}\sum\limits_{j}\text{P}_{\text{ij}}\times\left| \text{i}-\text{j}\right|\) & Linear correlation with contrast. \\ Angular Second Moment & \(\text{Asm}=\sum\limits_{i}\sum\limits_{j}\text{P}_{\text{ij}}^{2}\) & Reflects the thickness and fineness of image texture, with a focus on the low uniformity of internal texture in large surface water bodies. \\ \hline \end{tabular}
\end{table}
Table 2: Texture feature expressions and descriptions.