# 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
#### 5.3.2 Multiple-Set Canonical Correlation Analysis  
The PCCA has given better performance than PCA as evident in Section 5.2. We have applied PCCA on four views of every HSI dataset by selecting two views at a time and reduced the dimensions. On pairing the reduced features with SVM for per pixel prediction i.e., semantic segmentation, it outperforms other machine learning algorithms and gives good performance. However, when working with more than two views, sequential application of pairwise CCA might lead to information loss, as the joint interdependencies among all datasets are not considered simultaneously. In such cases, MCCA becomes advantageous, offering a more comprehensive exploration of relationships in multi-view datasets. The comparison of different existing ML techniques has been evaluated against different number of Canonical Variables i.e., Number of Components. Tables 10-12 highlight the proposed technique i.e., the fusion of MCCA-SVM robustness over other existing techniques. Table 10 compares OA of MCCA with different ML techniques on IP where it is evident that SVM when combined with MCCA gives the highest OA of 85.75% followed by RF with OA of 83.41% for number of components = 3. Similarly, Table 11 highlights supremacy of SVM for PU, with an OA of 90.90% followed by RF with OA of 84.59%. For SA, SVM with MCCA recorded the highest OA of 93.88%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of** & **MCCA-SVM** & \multirow{2}{*}{**MCCA-RF (\%)**} & **MCCA-Naive** & **MCCA-KNN** \\
**Components** & **(\%)** & & **Bayes (\%)** & **(\%)** \\ \hline
3 & 85.75 & 83.41 & 59.12 & 79.07 \\
4 & 89.41 & 87.51 & 63.17 & 82.92 \\
5 & 89.56 & 87.65 & 63.27 & 85.02 \\
6 & 88.92 & 87.85 & 63.07 & 83.95 \\
7 & 88.24 & 87.24 & 62.24 & 84.09 \\
8 & 88.09 & 87.12 & 64.48 & 84.14 \\
9 & 88.48 & 87.31 & 63.65 & 83.41 \\
10 & 87.46 & 86.63 & 63.75 & 82.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of Proposed Technique (MCCA-SVM) with Existing Techniques on IP dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of** & **MCCA-SVM** & \multirow{2}{*}{**MCCA-RF (\%)**} & **MCCA-Naive** & **MCCA-KNN** \\
**Components** & **(\%)** & & **Bayes (\%)** & **(\%)** \\ \hline
3 & 90.90 & 84.59 & 71.76 & 82.24 \\
4 & 92.66 & 88.94 & 74.66 & 86.61 \\
5 & 95.07 & 92.85 & 79.13 & 91.07 \\
6 & 94.92 & 93.27 & 80.31 & 91.70 \\
7 & 94.79 & 92.97 & 80.58 & 90.76 \\
8 & 94.55 & 91.87 & 81.42 & 89.60 \\
9 & 94.50 & 91.85 & 82.46 & 89.25 \\
10 & 94.43 & 91.68 & 82.41 & 88.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of Proposed Technique (MCCA-SVM) with Existing Techniques on PU dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of** & **MCCA-SVM** & \multirow{2}{*}{**MCCA-RF (\%)**} & **MCCA-Naive** & **MCCA-KNN** \\
**Components** & **(\%)** & & **Bayes (\%)** & **(\%)** \\ \hline
3 & 93.88 & 92.60 & 82.84 & 89.59 \\
4 & 94.45 & 93.54 & 85.53 & 90.57 \\
5 & 94.67 & 93.78 & 85.82 & 90.61 \\
6 & 94.64 & 94.41 & 86.50 & 90.21 \\
7 & 94.62 & 94.61 & 88.34 & 90.66 \\
8 & 94.88 & 94.69 & 88.98 & 90.95 \\
9 & 95.08 & 95.26 & 88.25 & 91.09 \\
10 & 95.04 & 95.60 & 89.34 & 91.02 \\ \hline \The superiority of the fusion of MCCA and SVM is also evident in the Class-wise accuracies of the HSI dataset for No. of Components = 3 as shown in Tables 13-15. The proposed technique of MCCA-SVM records an AA of 85.75% on IP as shown in Table 13, which is the highest as compared to AA of 80%, 64.00% and 79.50% of RF, Naive bayes and KNN. For PU in Table 14, MCCA-SVM give the highest OA of 88.50% in comparison with the AA of 79%, 73% and 78% of RF, Naive Bayes and KNN. Amongst all the datasets used in the research, highest AA of 97.00% is obtained for SA as shown in Table 15. RF records the second best AA of 96.00% followed by KNN with an AA of 94.00%.