# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 5 Experimental Results and Discussion
### Comparison of Proposed Technique with Existing Techniques
#### 5.3.1 Pairwise Canonical Correlation Analysis  
In PCCA, the analysis is conducted independently for each pair of views of dataset, seeking canonical variables that maximize the correlation between the two views. The comparison of different ML algorithms has been evaluated against different number of Canonical Variables i.e., No. of Components. This approach explores the relationships between pairs of datasets individually, providing insights into the shared information between each distinct combination. It has aided us in dimension reduction which enabled semantic segmentation of SVM through reduced, extracted features. Tables 4-6 show the robustness of SVM to handle high dimensionality and with appropriate kernel selection enhancing its performance compared to other machine learning classifiers. Table 4 compares OA of PCCA with different ML techniques on IP where it is evident that SVM when combined with PCCA gives the highest OA of 77.02% followed by RF with OA of 71.80% for number  
Figure 8: Comparison of PCA and CCA on SA dataset (**a**) Ground truth, (**b**) Segmentation map after PCA-SVM (**c**) Segmentation map after PCCA-SVM, (**d**) Segmentation map after MCCA-SVM. of components = 3. Similarly, Table 5 highlights supremacy of SVM for PU, with an OA of 89.95% followed by RF with OA of 86.58%. For SA, SVM with CCA recorded the highest OA of 92.15%. The superiority of the fusion of PCCA and SVM is also evident in the Class-wise accuracies of the HSI dataset for No. of Components = 3 as shown in Tables 7-9. The proposed technique of PCCA-SVM records an AA of 78.00% on IP as shown in Table 7, which is the highest as compared to AA of 70%, 64.00% and 70.50% of RF, Naive bayes and KNN. For PU in Table 8, PCCA-SVM give the highest OA of 87.00% in comparison with the AA of 80%, 72% and 78% of RF, Naive Bayes and KNN. Amongst all the datasets used in the research, highest AA of 96.00% is obtained for SA as shown in Table 9. RF records the second best AA of 95.00% followed by KNN with an AA of 94.00%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of** & \multirow{2}{*}{**PCCA-SVM (\%)**} & \multirow{2}{*}{**PCCA-RF (\%)**} & \multicolumn{2}{c}{**PCCA-Naive**} & \multirow{2}{*}{**PCCA-KNN (\%)**} \\
**Components** & & & & \\ \hline
3 & 92.15 & 91.32 & 81.36 & 89.54 \\
4 & 91.20 & 88.92 & 79.26 & 85.61 \\
5 & 90.33 & 85.10 & 68.53 & 78.59 \\
6 & 89. The segmentation maps generated after application of PCCA and different machine learning techniques has been shown in Figures 9-11. The proposed technique gives better segmentation maps in comparison to other techniques. In Figure 9 PCCA-SVM produces the least distorted segmentation map of IP. The segmentation map of Naive Bayes and KNN produce the noisiest segmentation results with highest misclassified pixels. Similarly, for PU and SA in Figures 10 and 11, the proposed technique produces segmentation map as closest to the Ground Truth indicating more accurate semantic segmentation of the landcover. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **PCCA-RF (\%)** & \begin{tabular}{c} **PCCA-Naive** \\ **Bayes (\%)** \\ \end{tabular}  & \ \begin{tabular}{c} **PCCA-KNN (\%)** \\ **PCCA-SVM** \\ **(\%))** \\ \end{tabular}  & \begin{tabular}{c} **Proposed** \\ **Technique** \\ **(PCCA-SVM** \\ **(\%))** \\ \end{tabular}  \\ \hline
1 & 67.00 & 100.00 & 83.00 & 69.00 \\
2 & 65.00 & 16.00 & 68.00 & 73.00 \\
3 &  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**PCCA-RF (\%)**} & \multirow{2}{*}{**PCCA-Naive Bayes**} & \multirow{2}{*}{**PCCA-KNN (\%)**} & \multirow{2}{*}{**PCCA-KNN (\%)**} \\  & & & & **(\%)** \\ \hline
1 & 96.00 & 83.00 & 95.00 & 99.00 \\
2 & 9  
Figure 11: Comparison of Proposed Technique (PCCA-SVM) with Existing Techniques on SA dataset. (**a**) Ground Truth, (**b**) Segmentation map after SVM, (**c**) Segmentation map after RF, (**d**) Segmentation map after Naive Bayes, (**e**) Segmentation map after KNN.