## 3 The Proposed Method
### ROI Extraction  
Using the parameters PRDV and PV, the ROI of ports can be extracted by detecting the port water region. Using the parameter PV, the water and land can be separated first. The interference water can then be separated from the normal water using the parameter PRDV. Since the difference of PV between the water and the land is usually large, PV can be segmented using a global thresholding method such as Ostu or a region-based method such as level set segmentation. If the whole histogram of the image satisfies a two-peak distribution, correct segmentation can be easy to obtain using a two-region segmentation method. However, the two-peak distribution is difficult to satisfy since the land consists of the urban area, high vegetation, low vegetation, and soil, and the water consists of normal water and interference water. Thus, the global CFAR thresholding segmentation method is used to segment the water and land. In the method, the water is taken as a background, and the land is taken as a target. Unlike the traditional CFAR work in a sliding window with a constant size under a false alarm rate (FAR), the method has a threshold by fitting the distribution of the background in a sampling region but segments the whole image plane using that threshold. As the volume power is approximately equal to the power of the cross-polarization component according to (6), the probability distribution of the volume power of the water is fitted to a gamma distribution. However, samples from different water areas in different image scenes are needed to estimate the parameters of the average power and the equivalent number in the gamma distribution. If the sampling regions are only selected from the region of calm water, land cannot be detected regardless of how large the false alarm rate is set since the volume scattering power of the calm water is much lower than that of land. Statistical results shown in Figure 5a show that the difference of the average PV between the land and the calm water only varies in a certain range. Therefore, we propose to obtain the threshold by adding a constant to the average PV of a sampling water region. To obtain the sampling region using an unsupervised approach, a patch of size \(N\times N\) in the center of the pixel is selected for each pixel. We then find the patch that the mean multiply variance is minimum. That is, the homogeneity measure of the mean and variance of the patch is defined as follows:  
\[H=\mu\sigma, \tag{8}\]  
where \(\mu\) is the mean, and \(\sigma^{2}\) is the variance of the span of a patch. The patch with the smallest homogeneity is selected as the sampling region. If the average PV of the sampling region is \(\
u\), the threshold of PV for water extraction \(th_{PV}\) is then set as \(\
u+\text{C}\). Our statistical results show that C should be set in the interval from 5 to 10 dB. Since the difference of the PRDV between the interference water and the normal water is large, the PRDV can also be segmented using the global CFAR method. However, the explicit probability density function (PDF) of the ratio parameter is difficult to deduce. Since the double-bounce power of the normal water is very low and the area percentage of the interference is very small, the PDF of the ratio parameter for the whole water region approximates a gamma distribution. Thus, the interference water can be detected by the global CFAR detection method, in which the interference water is taken as a target and the normal water is a background. However, the problem of the interference water extraction using the CFAR method is also that the sampling regions are difficult to obtain. The PDF of the PRDV parameter cannot be fitted using the same region as the water extraction because the value of the PRDV is close to zero in the normal water region. Thus, all the pixels of the water region are taken as samples for the PDF fitting of the PRDV parameter.