## 4 Results
### Results of Real SAR Images
Figure 6c-h illustrates different classification maps obtained by different test methods on the SAR2 image. As can be seen from Figure 6b, the SAR2 includes three types of land cover: crop, vegetation, and water. In the ground truth Figure 6a, the crop is marked as the first category, and is represented by gold in the classification maps of SAR2. The vegetation is identified as the second category, which is indicated by orange in the classification maps of SAR2. The water is the third class, which is shown by purple in the classification map of SAR2. Observed from Figure 6c,d, the SVM and CKS-FCM classification maps are very noisy because both do not clearly reflect nonlinear information. The CKFF in Figure 6e can deliver a comparatively clear result, but there are noise points in the local area, e.g., second class, vegetation, at the top right of the classification map. CKFF does not extract context spatial information and thus region smoothness is insufficient. Although the SVM-CK and MWSG algorithms of Figure 6f,g show improvements in the reduction of speckles, there are still deviations in local area details, e.g., third class, water. Both do not optimize the local of image and do not utilize generalized spatial information. By contrast, the proposed MAKC method in Figure 6h can provide the best visual performance in these areas, being more accurate in details, with great suppression of noise. This is due to the diversity of information captured by MAKC and the enhancement of local consistency. In the red box, the classification region edge of the proposed method is smoother than other methods and closer to the ground truth, and has less noise points. The corresponding quantitative results are shown in Table 4 with the best results in bold. As can be seen, compared with other methods, the proposed MAKC method exceeds the average of all methods by 4.3%, 4.5%, and 6.8% in terms of AA, OA, and Kappa with the highest values, respectively. In addition, another experiment is conducted on the SAR3 image in Figure 7b; the image consists of four types of land cover: water, vegetation, crop, and bare land. In ground truth Figure 7a, this water is labeled as the first category, and is represented by blue in classification maps of SAR3. Vegetation belongs to the second category, and in ground truth and in the SAR3 classification maps is purple. Crop is the third category and is orange in ground truth and in classification maps. The fourth category of bare land is in gold in ground truth and the classification maps. The quantitative results are represented in Table 5 with the best results in bold. As can be observed, the proposed MAKC method outperforms all the compared methods in terms of AA, OA, and Kappa when a small number of training samples is available. There are two main reasons for this. On the one hand, multi-feature can well retain the geometric characteristics of SAR images to provide more discriminative information for classification. On the other hand, the discriminative information is sufficiently utilized by mining the abundant spatial information within each feature and fusing the complementary information among features. A visual performance \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CLASS** & **SVM** & **CKS-FCM** & **CKFF** & **SVM-CK** & **MWSG** & **MAKC** \\ \hline \(\downarrow\) crop & 92.43 & 83.32 & 89.92 & 93.00 & 88.31 & 95.79 \\ \(\downarrow\) vegetation & 79.29 & 74.28 & 92.23 & 95.56 & 95.22 & 94.85 \\ \(\downarrow\) water & 85.33 & 98.20 & 92.40 & 92.06 & 94.76 & 94.11 \\ AA & 85.68 & 85.27 & 91.51 & 93.54 & 92.76 & 94.92 \\ OA & 84.71 & 83.78 & 91.63 & 93.82 & 93.16 & 94.90 \\ Kappa & 0.77 & 0.75 & 0.87 & 0.91 & 0.89 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification precision results on SAR2.