## 1 Introduction  
Synthetic aperture radar (SAR) can acquire images of different land covers through clouds and rain in all weather conditions and times, and it has a certain surface penetrating capability. Therefore, SAR images are widely used in disaster monitoring, environment mapping, urban planning, military reconnaissance, and crop yield estimation [1]. SAR image classification is one of the core contents of SAR image interpretation. However, with SAR image classification it is not easy to obtain satisfactory results, due to the speckle embedding in SAR imaging. This remains a challenging task to be resolved. In recent years, the research on SAR image classification technology has mainly focused on two crucial aspects: more effective representation of feature information, and enhancing the local consistency on pixel labels. To date, various methods have been proposed for feature information representation in classification. The most basic feature information representation is various feature extraction, which includes gray-level co-occurrence matrix (GLCM), Gabor filters (GFs), wavelet (WL), attribute profiles (APs), etc. GLCM was employed for retrieval of sea surface wind direction from SAR images [2], and GFs were used for SAR images to detect change [3]. Discrete wavelet transform processing on coherent sea-ice textures, while being decomposed, has been utilized to perform fractal analysis of SAR images [4]. APwas used to distinguish heterogeneous and homogeneous image contents in SAR image segmentation by [PERSON] et al. [5], while [PERSON] et al. [6] investigated AP for pixel-based SAR image classification. These features always retain image detail features, which do not fully exploit the contextual and spatial information to suppress the speckle effect. In recent years, the representation of feature information has gradually developed to mine context information. The Markov random field (MRF) has been widely used in SAR image classification to exploit the context information [7]. The Markov random field model was used to cluster the SAR image, and according to the centroid of the region, the region category was located with complete edge [8]. However, this method always considers the neighborhood to be usually in the small fixed-size, which is not very useful to preserve the structures. In addition, the composite kernel support vector machine (SVM) is utilized to exploit the context information [9; 10], such as the correlation between pixels, when it classifies SAR images. According to experience set parameters, a composite kernel was constructed, and it explores both the two elements of information in the adaptive region: the texture feature and the context [9]. Moreover, a composite kernel method was proposed to combine the polarimetric and morphological feature fusion [10] with adjustment parameter of experience. However, they both set the kernel parameters artificially in the construction of the composite kernel, the contribution of different features is balanced by experience, and the feature difference cannot be reflected autonomously, so they cannot accurately express the image information. In order to exploit the information in different aspects, multiple features are further joined for representation. Multi-feature was fused in low-rank representation for SAR target recognition [11], and it was utilized to classify the SAR image [12], improving the classification accuracy of the SAR image. However, the multiple features are united in the simple stacking way, and the difference between the multiple features cannot be taken into account. The method cannot fully mine the feature information, and the essence of the image cannot be better highlighted. Although the approaches above can effectively improve the classification accuracy, \"salt and pepper\" noise often appears in their classification results due to multiplicative speckle.