## 4 Results
### Results on Synthetic SAR Images  
In this section, the experiments performed on three synthetic SAR images are detailed. All of the three synthetic SAR images are corrupted by the complicated simulated noise with three-looks, which follows the multiplicative speckle rule [20]. The test synthetic images are named Syn1, Syn2, and Syn3, and contain different numbers of texture types: 2, 4, and 8, respectively. The size of each image is 512 \(\times\) 512 pixels. To evaluate the classification performance, training samples are selected randomly in all of the comparison methods, taking fifty samples for each class. Moreover, the averages of the classification accuracies over ten runs are reported. The classification maps are shown in Figure 4, and the classification results for the overall accuracy and the Kappa coefficient are shown in Tables 1 and 2, respectively. Figure 4: \(Cont.\)  
For the Syn1, results can be observed in Figure 4a1-h1, which is a two-class synthetic image. Figure 4a1 shows the ground truth image corresponding to Figure 4b1. The classification result of SVM is very poor, as shown in Figure 4c1. This is because SVM does not consider spatial information. Figure 4d1 shows the classification result of CKS-FCM, which has many spots in consistent regions. CKS-FCM combined with non-local spatial information has certain noise suppression, but it is still not ideal. Although Figure 4e1,g1 obtained by CKFF and MWSG, respectively, are better than Figure 4c1,d1, some pixels are obviously misclassified in consistent regions. CKFF and MWSG both take advantage of multi-feature information, CKFF stacks features simply, and MWSG eliminates singular data for computational fusion. More information is utilized than in SVM or CKS-FCM. In Figure 4f1, the SVM-CK obtains classification regions with no noise, but the edge of the segmentation regions is rough. SVM-CK only uses context information without feature fusion to emphasize the details. In contrast, the classification of MAKC shown in Figure 4h1 has good regional consistency and no noise. MAKC not only uses context information, but it is not clear how to detect the features. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Syn1 AOA** & **Syn2 OA** & **Syn3 OA** \\ \hline SVM & 85.85 & 75.75 & 57.37 \\ CKS-FCM & 97.21 & 98.94 & 92.1 \\ CKFF & 98.91 & 92.26 & 92.85 \\ SVM-CK & 98.76 & 96.69 & 95.9 \\ MWSG & 99.31 & 97.66 & 97.14 \\ MAKC & 99.33 & 99.68 & 99.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall accuracies of different algorithms on the synthetic SAR images. Figure 4: Results of the synthetic SAR images: (**a1–a3**) ground truths; (**b1–b3**) synthetic SAR images; (**c1–c3**) SVM; (**d1–d3**) CKS-FCM; (**e1–e3**) CKFF; (**f1–f3**) SVM-CK; (**g1–g3**) MWSG; (**h1–h3**) MAKC, where the numbers represent the sequence index of the images. but also makes use of generalized spatial information, and adjust the details by local consistency optimization of multiple feature fusion. Figure 4b2 shows a four-class simulated SAR image named Syn2, and the corresponding ground truth is shown in Figure 4a2, which contains four gray levels 0, 77, 179, and 255. The classification results of SVM and CKFF are very poor, as shown in Figure 4c2,e2, respectively. The four regions are not divided by SVM, and CKFF has dense noise points. The four different regions in Figure 4d2,g2 obtained by CKS-FCM and MWSG, respectively, can be separated. However, their classifications have a large number of pixels misclassified. In the classifications of SVM-CK in Figure 4f2, the edge of regions is fuzzy, and there are chaotic points here. The classification result of MAKC has better regional consistency than others, as shown in Figure 4h2. In the Syn2 image, the texture contour is varied, and the type is complex; SVM fails to embody nonlinear information among data, and CKFF does not mine spatial context information and is sensitive to noise under complex data.