## 1 Introduction
A good way to solve this problem is to introduce the feature space and combine features dynamically according to difference between features, which can effectively suppress the noise of classification maps and make full use of feature information. Another aspect is to improve the local consistency of pixel labels. Typical methods include over-segmentation techniques used in the preprocessing step and optimization based on probability theory performed as a postprocessing procedure. The over-segmentation technique divides the whole SAR image into small uniform patches or superpixels, which is an indispensable part of the subsequent classifier learning and labeling process [13; 14; 15]. In the segmentation process, pixels in each patch share the same label. Optimization based on probability theory integrates prior label smoothing into the segmentation task to solve the largest probability [16; 17; 18]. Dempster-Shafer evidence theory makes up for the deficiency caused by the incomplete, partially inaccurate, or uncertain information, and the hypothesis with the maximum credibility and likelihood degree is selected as the fusion result [16], but it is sensitive to basic reliability allocation. Graph optimization integrates prior information into graph construction, solves the maximum posterior probability to enhance the local consistency of labels, and obtains segmentation results with higher classification accuracy and better spatial connectivity [17]. However, the construction of graph structure takes more time and requires high computational cost. Majority vote algorithms are simple to calculate, flexible in resolving conflicts [18; 19], and do not require the allocation of confidence. Both the above two aspects can effectively promote the performance of SAR image classification. However, most of the existing SAR image classification methods usually consider only one of them. In this paper, to address the aforementioned problem for SAR image classification, we propose a classification framework of multi-feature fusion and adaptive kernel combination (MAKC), which constructs the feature space to fully mine multi-feature information while using optimization to ensure the local consistency of labels. The main contributions are summarized as follows: (1) Constructing three-dimensional feature space of generalized spatial information for fusion, meaning space information is utilized more deeply than ever before, instead of only plane contextual information. (2) Adaptive kernel combination reflects the feature difference to mine the spatial information, with dynamic weights according to image content rather than artificial setting according to experience. (3) Different from the original over-segmentation algorithm, it combines the Gamma distribution of SAR images and introduces non-negative logarithmic likelihood value to evaluate pixel similarity, which makes the algorithm more suitable for SAR images. (4) Compared with the previous postprocessing optimization procedure that only considers confidence level, a conflict resolution rule that can comprehensively consider the quality of features enhances the local consistency of pixel labels to fuse features, and also changes the traditional stacking way of feature fusion. The rest of this paper is organized as follows. In Section 2, we review the notation and background materials. In Section 3, the proposed method is detailed. In Section 4, the experimental results are given and compared with several state-of-the-art classification methods. Then, in Section 5, the effectiveness of the method step and the adjustment of parameters are discussed. Finally, in Section 6, the paper is summarized and future works are suggested.