## 3 Experimental Study
### Experimental Results Presentation and Discussion  
The first experiment is tested on a synthetic SAR image with eight classes and 260 \(\times\) 260 pixels, as shown in Figure 3a. The artificial SAR image with three-looks follows the [PERSON]'s multiplicative speckle noise model [24]. It is clear that the three variants of FCM cannot achieve good segmentation results of the image. There are lots of misclassified spots at different patches in the image. The reason may be that the similarity measures designed by incorporating local spatial and gray information cannot produce enough suppressing results of the speckle noise. It seems that KGC in Figure 3e can recognize most classes of the synthetic image, however, two white local regions at the right down of the image are not well separated, _i.e._, they are seriously mixed together. Besides, the positive factor \(\alpha\) to adjust the weight of the prior information is hard to set to an appropriator value for different tested problems. In fact, the proposed algorithm seems to have presented relatively better segmentation result not only in region consistence but also in the boundary localization.  
Figure 3: The segmentation results of the synthetic SAR image. **(a)** Original image; **(b–f)** segmentation results by KWFLICM, FLICM, FGFCM, KGC, and MKCIS, respectively.  
In addition, the segmentation results of the five algorithms over repeated independent runs are also tested here. Table 2 shows the statistical mean and standard deviation of segmentation results over 30 independent runs. Besides, the index: accurate rate (AR) and an information theory induced index, called entropy (E) are used to calculate the clustering performance for the algorithms. The statistical results in Table 2 also agree with the visually inspection in Figure 3.  
Another experiment is carried out by using a real SAR image from the website of German Aerospace Center. It has 1-m spatial resolution at _X_-band of Swabian Jura, Germany by Terra-SAR. The image with 280 \(\times\) 280 pixels consists of four distinct typical land covers: urban villages and four types of crops.  
The segmentation results of the five algorithms in segmenting the real SAR image are shown Figure 4 and the statistical segmentation results over many repeated independent runs are listed in Table 3. Note that the ground truth of SAR images are usually absent or difficult to obtain, therefore, the statistical results of accuracy rate are not listed in Table 3. It is clear that KGC and MKCIS have achieved the better segmentation results that other three algorithms. And the similar phenomenon is also shown in statistical results in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**T** & **KWFLICM** & **FLICM** & **FGFCM** & **KGC** & **MKCIS** \\ \hline AR & 85.12 (0) & 65.28 (0.52) & 78.15(0) & 78.95(24.5) & 98.14(0.01) \\ E & 2.58(0.01) & 2.59 (0.01) & 2.59(0) & 2.59(0.15) & 2.55(0.2) \\ \hline \end{tabular}
\end{table}
Table 2: Statistical segmentation results of accurate rate (AR) and entropy (E) of the synthesized SAR image over 30 independent runs.  
Figure 4: The segmentation results of the real SAR image. (**a**) Original image; (**b–f**) segmentation results by KWFLICM, FLICM, FGFCM, KGC, and MKCIS, respectively.