## 3 Methodology
### Proposed Framework  
The proposed framework of the study mainly included a few steps (Figure 6).  
1. Landsat 8 OLI feature extraction: to obtain high-quality optical data, the Gram-Schmidt image-fusion method [31; 32] was used to fully utilize the spatial texture information via a panchromatic camera and the spectral information from the multispectral camera. On this basis, we calculated spectral features such as brightness value (BG), NDVI; gray-level co-occurrence matrix (GLCM) texture features, contrast, homogeneity, angular second moment, entropy, mean, dissimilarity, variance, and correlation. Simultaneously, we extracted the elevation and slope of different land-cover types in the study area.
2. ALOS 2 SAR data feature extraction: different ground targets in SAR images have different texture features. SAR images contain rich texture information, and texture features are important for image interpretation. We described SAR textures by studying the spatial-correlation properties of grayscale. We calculated the following eight texture features of the ALOS-2 image: contrast, variance, dissimilarity, angular second moment, mean, entropy, homogeneity, and correlation. In addition, the RGB images obtained using the Pauli decomposition were extracted.
3. Optical and SAR image feature level fusion: The OLI multi-spectral images are rich in hue and saturation information compared with high-resolution SAR images, but have less texture information. We can make full use of spectral information of OLI images and high-resolution SAR images.  
Figure 6: Land use/land cover (LULC) classification strategy in cloudy mountainous areas.  
polarization and texture information of ALOS-2 images, fuse SAR and optical pixels of the same name into multidimensional feature vectors at the feature level. The approach not only improves fusion effect, but also obtains richer feature information. The fusion features are involved in LULC classification to effectively improve accuracy of classification. In this study, two kinds of remote sensing images (ALOS-2 image, OLI image) were preprocessed and the features were extracted respectively. Then the principal component analysis (PCA) of the extracted features was performed, respectively. We retained the first three principal components, in which the features with low correlation and few redundant information. The optical and SAR texture information was superimposed with a certain weight, the spectral information of the first principal component of the optical image was enhanced by a specific weight, and then we added the features of the optical image to the first principal component of the SAR image in order to obtain the enhanced principal component. Then a local energy fusion strategy was used to fuse the texture components of the SAR image and the optical image to obtain the fused texture components. After the processing of spectral and texture components was completed, the contourlet method [33] was adopted to fuse the prepared features. In this paper, we consider the feature information and correlation of images, and get more macroscopic feature level information compared with pixel level fusion.
4. LULC classification was conducted by using an improved SVM classifier. Owing to the nonlinear nature of high-resolution remote-sensing data, the classification of remote-sensing data is mostly a nonlinear classification problem. To solve the classification of linear indivisible problems, we can improve the parameters C and \(\gamma\) of the radial basis function (RBF) in the SVM classifier.
5. Analysis and comparison of the results. We compared and analyzed the results derived using the method proposed in this study, where the classification results was obtained using a single image.