## 1 Introduction  
Using remote-sensing data to quickly and accurately obtain land use/land cover (LULC) information can provide the basic data for studying the spatio-temporal changes in land use and global change [1]. The mountains in southwestern China (Chongqing, Sichuan, Guizhou, etc.) are affected by clouds and fog [2], only a few optical remote sensing images are available. Furthermore, the complex terrain severely affects the optical and microwave images [3], there is a large difference in the effective light received by each pixel of the remote-sensing image [4]. This phenomenon has resulted in considerable difficulties in the extraction of mountain-surface information. In the early global and regional scales, LULC classification was mainly performed using normalized difference vegetation index (NDVI) of NOAA_AVHRR data or combined with the data obtained using other channels of AVHRR. On the basis of these data, researchers have conducted considerable research [5], however, the low spatial resolution (1 km) resulted in many uncertainties in the LULC classification. Landsat MSS/TM/ETM+, which has been shared for free globally since 2008, and terrain orthorectification products (L1T products) of the operational land imager (OLI) series satellites successfully launched in 2013 were used. The Landsat series data are more widely used for land-cover-change monitoring. [PERSON] [6] used the Landsat OLI panchromatic and multispectral fusion data to extract the crops by using decision trees, support vector machines (SVMs), and random forests. [PERSON] [7] used the Landsat OLI data to conduct LULC classification in Beijing, the results showed that compared with Landsat TM/ETM+ data, the new features of the Landsat OLI data are helpful in improving the accuracy of LULC classification. Since the 1990s, the technology of synthetic aperture radar (SAR) has developed rapidly and become a useful supplement to optical remote sensing. This technology has strong penetration imaging characteristics [8] and an added advantage that it can generate images during almost all weather conditions. Besides, it will not be affected by cloud or rain to a certain extent as well. The SAR has an added advantage that it can penetrate the surface to a certain extent depending upon the operating frequency and the dielectric constant of the target, SAR images contain useful information that cannot be found in optical images. At the beginning of the development of SAR remote sensing, the LULC classification could only work on the basis of multi-temporal, single-polar SAR data, i.e., to fully use the time-series characteristics of the backscatter coefficients of various types of surface features. In recent years, the SAR technology [9; 10] has been developed to achieve high-resolution, multi-mode, and multi-polarization features. Not only can the shape of the ground object be retrieved by the power of the ground surface echo, but also the relative phase information between different polarization channels can be used to reflect the medium electrical properties and other characteristics [11]. Furthermore, dual-polarization and full-polarization SAR data contain more complete physical and structural information of the target, providing new technical methods [12; 13] for dynamic land-use monitoring and LULC classification. SAR LULC classification methods are mainly divided into two types [14; 15], one is to directly use the backscattering matrix or polarization covariance matrix and polarization coherence matrix of the SAR data; the other is to perform polarization coherence decomposition on the basis of different matrices in order to obtain classification features, combined with classifier classification. Data fusion is critical to improving the application ability of remote-sensing images, and it has been a research hotspot in the domain of remote-sensing information processing and application for a long time. Optical and microwave remote sensing are two of the most common methods for obtaining the surface information.