## 6 Conclusions  
In this paper, a new multi-feature fusion classification framework is proposed to exploit the contextual and generalized spatial information, and dynamically mine nonlinear information by adaptive kernel combination on SAR images, while at the same time enhancing the local consistency, which is termed the MAKC method. First, three discriminant and complementary texture information tensors can be constructed by extracting APs, GLCMs, and WLs from an SAR image. Then, 3D feature blocks are obtained by segmentation mapping based on the gamma distribution and non-negative logarithmic  
Figure 10: Effects of the number of original superpixels on (**a**) SAR1 image (TN = 50), (**b**) SAR2 image (TN = 50), and (**c**) SAR3 image (TN = 10, TN = 30, and TN = 50). TN = n means the number of training samples is equal to n.  
Figure 9: Classification result (in OA, AA, and Kappa) comparison among AP, GLCM, WL, and MAKC, with different numbers of training samples. This is demonstrated on (**a**) SAR1 image, (**b**) SAR2 image, and (**c**) SAR3 image. TN = n means the number of training samples is equal to n.  
likelihood difference on the SAR image. Feature spaces of context and generalized spatial information are constructed, respectively; adaptive kernel combination explores nonlinear information, and the information within features is fused. Furthermore, local consistency optimization is implemented to fuse complementary information among features. In this way, the different spatial information within each feature group, the abundant texture information of the SAR image, and the complementary information among feature groups are well utilized. The classification results of the proposed MAKC on three synthetic SAR images and three real SAR images are better than several state-of-the-art classification methods in both quantitative and visual performance, which proves the effectiveness of this method.  
In the proposed framework, the multi-feature adaptive kernel combination is used to explore the spatial and nonlinear information within the feature tensors. Our future work will take advantage of the framework with other features and combine them with other classifiers to improve classification accuracy, e.g., neural networks. In addition, since the feature information is a tensor, tensor analysis should be studied to extract more discriminant spatial information.  
Methodology, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]); validation, H.X., L.Y., and C.G.; writing--original draft, [PERSON] ([PERSON]); writing--review & editing, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was funded in part by the National Natural Science Foundation of China under Grant 61472278, in part by the Major Project of Tianjin under Grant 18 ZXZNGX00150, in part by the Natural Science Foundation of Tianjin under Grant 18 JCYBJC84800, in part by the Scientific Research Key Project of Tianjin Municipal Education Commission under Grant 2017 ZD13, and in part by the Scientific Research Project of Tianjin Municipal Education Commission under Grant 2017 KJ255.  
Not applicable.  
The authors declare no conflict of interest.