## 4 Results
### Results of Real SAR Images  
Three real SAR images (SAR1, SAR2, and SAR3) are tested. SAR1 has a size of 475 \(\times\) 446 pixels, and was taken on 1 April 1993. It covers the Naval Air Weapons Station China Lake, CA, at the latitude 35\({}^{\circ}\)41\({}^{\prime}\)14.25\({}^{\prime\prime}\) north, the longitude 117\({}^{\circ}\)41\({}^{\prime}\)92.27\({}^{\prime\prime}\) west, with Ku-band radar with a 3-m resolution. The image includes three types of ground objects of runway (dark), bare land (gray), and infrastructures (bright). SAR2 has a size of 256 \(\times\) 256 pixels, and was taken on 13 August 1997 with C-band radar with a 4-m resolution, and covers a suburb of Beijing, China, about latitude 40\({}^{\circ}\)04\({}^{\prime}\)43.44\({}^{\prime\prime}\) north, longitude 116\({}^{\circ}\)11\({}^{\prime}\)28.43\({}^{\prime\prime}\) east. This image includes three types of land cover: water (dark), crop (gray), and vegetation (bright). SAR3 has a size of 505 \(\times\) 476 pixels and was taken on 19 June 2007 near to latitude 48\({}^{\circ}\)31\({}^{\prime}\)13.99\({}^{\prime\prime}\) north, longitude 43\({}^{\circ}\)28\({}^{\prime}\)13.86\({}^{\prime\prime}\) east. It covers an area of the south Russian steppes northeast of the Black Sea with a 15-m resolution X-band radar. The image consists of four types of land cover: water (dark), vegetation (dark gray), crop (light  
For the SAR1, results can be observed from Figure 5b, which includes three types of ground objects of runway, bare land, and airport infrastructures. Figure 5a shows the ground truth image corresponding to Figure 5b. The runway is marked as the first category, and is represented in purple in the classification maps of SAR1. The bare land is identified as the second category, which is indicated by gold in the classification maps of SAR1. The airport infrastructures are the third class, which is shown by orange in the classification map of SAR1. As can be observed, the SVM method of Figure 5c shows a very noisy estimation in its classification map because it only considers the pixel intensity information, with no combination of spatial information. By incorporating the non-local mean to suppress the noise of the SAR image, the CKS-FCM of Figure 5d delivers a smoother visual result. However, it fails to identify the region area although the edge is clear, e.g., the third class, airport infrastructures. By combining nonlinear spatial information, the CKFF in Figure 5e and the SVM-CK in Figure 5f have better visual performance, but still have some incorrect classification labels, e.g., confusion between bare land and airport infrastructures. By expressing the overall spatial information of the SAR image, MWSG in Figure 5g achieves a good result, but fails to identify the pixels in the detailed and edge regions, e.g., the first class, runway. By contrast, the proposed MAKC method in Figure 5h has the best visual classification performance, which not only reduces the speckles greatly but also preserves the meaningful structural information. Because the multi-feature description image is comprehensive, and nonlinear spatial information is mined richly, the local details of the image are considered through consistency enhancement, so the MAKC method achieves convincing results. The runway contour and area classification of the proposed method in the red rectangle are obviously better than those of other methods. Table 3 shows the corresponding classification accuracy of all methods, in which the best results are shown in bold. As can be seen, the proposed MAKC method achieves the highest classification accuracy in terms of AA, OA, and Kappa. The average accuracy is more than 7 percentage points higher than other methods, and the overall accuracy is more than 3 percentage points higher than other methods. Figure 7: Results of a real SAR image: (**a**) ground truth; (**b**) real SAR image; (**c**) SVM; (**d**) CKS-FCM; (**e**) CKFF; (**f**) SVM-CK; (**g**) MWSG; (**h**) MAKC.