# Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data
## 4 Results and Discussions
### Lithological Classification Using Selected Features  
The texture and spectral images acting as input vectors were fused in the SVM classifier for lithological classification. Then, the Landsat 8 imagery was classified into four rock types, including gabbro, serpentinite, granite, and quartzose rock. The classification results are shown in Figures 7 and 8. The classification accuracy was calculated based on the confusion matrix and was presented in Tables 4 and 5. The results show that the classification incorporating textural features is noticeably improved compared to that using only spectral vectors. The overall accuracy and Kappa coefficient (\(\kappa\)) increased by 9.04% and 0.13%, respectively. Moreover, all four rock objects were better identified, especially granite and quartzose rocks, which had the highest increase in classification accuracy, confirming the results presented in Section 4.2. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Pairwise Rocks** & **Series 1** & **Series 2** & **Series 3** & **Series 4** & **Series 5** & **Series 6** & **Series 7** \\ \hline Ga-Se & _SF + TF4_ & _SF + TF47_ & _SF + TF467_ & _SF + TF4567_ & _SF + TF14567_ & _SF + TF124567_ & _SF + TF1_-7 \\ Ga-Gr & _SF + TF3_ & _SF + TF15_ & _SF + TF367_ & _SF + TF1345_ & _SF + TF13457_ & _SF + TF123457_ & _SF + TF1_-7 \\ Ga-Qu & _SF + TF2_ & _SF + TF23_ & _SF + TF236_ & _SF + TF23567_ & _SF + TF234567_ & _SF + TF1_-7 \\ Se-Gr & _SF + TF4_ & _SF + TF45_ & _SF + TF146_ & _SF + TF1467_ & _SF + TF12467_ & _SF + TF123467_ & _SF + TF1_-7 \\ Se-Qu & _SF + TF4_ & _SF + TF45_ & _SF + TF456_ & _SF + TF2456_ & _SF + TF124567_ & _SF + TF1_-7 \\ Gr-Qu & _SF + TF6_ & _SF + TF67_ & _SF + TF2356_ & _SF + TF2367_ & _SF + TF23567_ & _SF + TF1_-7 \\ \hline \hline \end{tabular}  
* Ga is gabbro, Se is serpentinite, Gr is granite, and Qu is quartzose rock; the expression _SF + TF1234567_ indicates the feature combination of seven spectral features fused with _TF1_, _TF2_, _TF3_, _TF4_, _TF5_, _TF6_, and _TF7_. The same convention applies to the rest of the table contents.  
\end{table} 
Table 3: Statistically significant J-M distance for the same number of vectors *  
Figure 6: Increases in class separability for seven series compared to results obtained using only spectral vectors. Ga is gabbro, Se is serpentinite, Gr is granite, and Qu is quartzose rock. Figure 8: Zoom-in and comparison of classification results of (**a**) SVM with seven spectral vectors and no textural feature; and (**b**) SVM with seven spectral vectors fused with seven textural vectors for gabbro, quartzose rock, and granite. The ellipses are used to highlight the changes between Figure 8 (**a**) and (**b**). Figure 7: Landsat 8 classification results of the study area using (**a**) support vector machine (SVM) with only seven spectral vectors and no textural vector (the overall accuracy is 74.69% and \(\kappa\) is 0.64); and (**b**) SVM with seven textural vectors fused with seven spectral vectors (overall accuracy is 83.73% and \(\kappa\) is 0.77). Improved performance is achieved when the selected feature vectors are integrated in SVM for lithological classification. This can be attributed to two factors. First, texture features improve our understanding of images, as they characterize the roughness or geometric complexity of objects in the remote sensing image. Second, the optimal combination of feature vectors is selected to act as the input vectors in a classifier. However, the universality of this strategy needs to be confirmed further. Only four common rock types are discussed in this paper, due to the limited size of the study area. Larger areas with additional rock types should also be taken into consideration. The concept of texture has been successfully introduced in remote sensing science. Textural features help to better characterize the surface morphology of surface objects.