## 4 Results
### Classification Results and Accuracy of the Proposed Method  
For Landsat OLI and ALOS-2 features fusion data, multi-scale segmentation was still used in this study. The weights of the three bands after segmentation and fusion were set to 1; the shape coefficient was 0.1 and the compactness 0.5. I tried three scales of 5, 15, 20, and 30. Through comparison and discrimination with the naked eye, the scales were over segmented at 5, and the scales of 20 and 30 could not represent the best contours of the features. In addition, the image-segmentation scale was set to 15, which can satisfactorily maintain the boundary of the features, as depicted in Figure 12.  
Figure 11: Classification features of synthetic aperture radar (SAR) and OLI optical images.  
Figure 12: _Cont._On the basis of the above, this study attempts to design an improved SVM method to classify fused images. By continuously selecting and optimizing the training samples, taking this sample as the center, continuously adjusting the core parameters in the SVM model, and after many experiments, the classification results were obtained, as depicted in Figure 13. The classification accuracy of each feature type is mainly measured by user accuracy (UA) and producer accuracy (PA). UA refers to any random sample from the classification result, which has the same conditional probability as the actual type on the ground, PA refers to the classifier's ratio of the sampled pixels of the image correctly classified into the category A to the total number of the samples classified into the category A. As can be seen from the enlarged view of land types, artificial surfaces, farmland, forest, shrubland, water are better distinguished, producer accuracy (PA) and user accuracy (UA) are relatively high, which meets the classification needs of mountain land cover types. Figure 14 shows the comparison of classification results, A is the classification of Landsat OLI image (cloudines 8.43%), B is classification of ALOS 2 image, C is classification of Fusion features image, compared with the classification results and accuracy of A and B, the classification results and accuracy of C are greatly improved excluding the shrubland class. This is because the hierarchical structure of shrubland is not as regular as forest, and texture features of shrubland vary greatly in large scale regions. The accuracy of shrubland based on OLI image is higher compared with ALOS-2 image and fusion feature datasets. Table 3 shows the accuracy validation of land-cover type classification results based on fusion image.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Forest** & **Shrubland** & **Gardenland** & **Farmland** & **Water** & **Artificial Surface** & **Others** & **Total** & **UA (\%)** \\ \hline Forest & 49 & 7 & 1 & 2 & 0 & 1 & 0 & 59 & 83.05 \\ Shrubland & 4 & 30 & 3 & 2 & 0 & 0 & 0 & 39 & 76.92 \\ Gardland & 0 & 1 & 19 & 3 & 0 & 0 & 0 & 23 & 82.61 \\ Farmland & 0 & 4 & 5 & 55 & 1 & 0 & 0 & 65 & 84.62 \\ Water & 0 & 0 & 0 & 0 & 27 & 1 & 0 & 28 & 96.43 \\ Artificial surface & 0 & 0 & 0 & 2 & 0 & 59 & 0 & 61 & 96.72 \\ Others & 0 & 0 & 0 & 1 & 0 & 0 & 8 & 9 & 88.89 \\ Total & 53 & 42 & 28 & 65 & 28 & 61 & 8 & 284 & \\ PA(\%) & 92.45 & 71.43 & 67.86 & 84.62 & 96.43 & 96.72 & 100.00 & & \\  & & & & & & & Kappa coefficient: 0.8447 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy validation of land-cover type classification results based on fusion image.  
Figure 12: Different scale-segmentation effects based on features fusion image. (**a**) Scales 15; (**b**) scales 25; (**c**) scales 35; (**d**) scales 50.  
Figure 14: _Cont._  
Figure 13: Classification results of fusion features.