# Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data
## 5 Conclusions  
Prior studies have demonstrated the capability of integrating textural features with spectral features for image classification and pattern recognition. However, these studies focus mostly on the improvement or design of texture-derived methods. In addition, the used textural features \begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Gabbro** & **Serpentinite** & **Granite** & **Quartzose Rock** & **Total** \\ \hline Gabbro & 730 & 0 & 87 & 97 & 914 \\ Serpentinite & 0 & 250 & 0 & 93 & 343 \\ Granite & 26 & 85 & 120 & 553 & 784 \\ Quartzose rock & 13 & 0 & 120 & 39 & 172 \\ Total & 769 & 335 & 327 & 782 & 2213 \\ \hline \multicolumn{5}{c}{Overall accuracy: 74.69\%; \(\kappa\): 0.6366} \\ \hline
**Class** & **Product Accuracy (\%)** & **User Accuracy (\%)** & **Commission (\%)** & **Omission (\%)** \\ \hline Gabbro & 94.93 (730/769) & 79.87 (730/914) & 20.13 (184/914) & 5.07 (39/769) \\ Serpentinite & 74.63 (250/335) & 72.89 (250/343) & 27.11 (93/343) & 25.37 (85/335) \\ Granite & 36.70 (120/327) & 69.77 (120/172) & 30.23 (52/172) & 63.30 (207/327) \\ Quartzose rock & 70.72 (553/782) & 70.54 (553/784) & 29.46 (231/784) & 29.28 (229/782) \\ \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix and accuracy evaluation of classification results using support vector machine (SVM) with seven spectral vectors. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Class** & **Gabbro** & **Serpentinite** & **Granite** & **Quartzose Rock** & **Total** \\ \hline Gabbro & 731 & 0 & 120 & 45 & 896 \\ Serpentinite & 0 & 267 & 29 & 0 & 296 \\ Granite & 32 & 68 & 608 & 35 & 743 \\ Quartzose rock & 6 & 0 & 25 & 247 & 278 \\ Total & 769 & 335 & 782 & 327 & 2213 \\ \hline \multicolumn{5}{c}{Overall accuracy: 83.73\%; \(\kappa\): 0.7682} \\ \hline
**Class** & **Product Accuracy (\%)** & **User Accuracy (\%)** & **Commission (\%)** & **Omission (\%)** \\ \hline Gabbro & 95.06 (731/769) & 81.58 (731/896) & 18.42 (165/896) & 4.94 (38/769) \\ Serpentinite & 79.72 (267/335) & 90.2 (267/296) & 9.8 (29/296) & 20.3 (68/335) \\ Granite & 77.75 (608/782) & 81.83 (608/743) & 18.17 (135/743) & 22.25 (174/782) \\ Quartzose rock & 75.54 (247/327) & 88.85 (247/278) & 11.15 (31/278) & 24.46 (8/327) \\ \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix and accuracy evaluation of classification results using SVM with seven spectral vectors fused with seven textural vectors. are calculated from a single remote sensing image, resulting in invalid results, which has been demonstrated in [PERSON] et al. [69]. In this paper, we focused on examining the sensitivity of different texture images to aid spectral features for lithological classification. So, the J-M distance was used to examine the performance of textural vectors derived from different sources and to select optimal combinations of textural and spectral vectors for lithology identification. The statistical results indicate that more textural vectors yield higher class separability. Then, multiple textural vectors and multiple spectral vectors were integrated using the SVM model to improve lithological classification. Classification results were evaluated based on the confusion matrix. Compared with conventional classification based only on spectral features, the classification accuracy obtained by integrated textural and spectral features is noticeably improved, especially for granite and quartzose rocks, which show an increase in classification accuracy of 38.84% and 7.03%, respectively, indicating that textural features provide useful supplementary information for lithological identification. We conclude that (i) textural features that reveal rock differences in surface morphology have a remarkable ability to improve lithological classification, especially for rocks that have subtle spectral features; (ii) texture images derived from different data sources have different sensitivities for lithological identification.