# 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
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**MCCA-RF (\%)**} & \multirow{2}{*}{**MCCA-Naive**} & \multirow{2}{*}{**MCCA-KNN (\%)**} & \multirow{2}{*}{**Proposed**} \\  & & & & **(MCCA-SVM (\%))** \\ \hline
1 & 90.00 & 70.00 & 84.00 & 90.00 \\
2 & 97.00 & 78.00 & 94.00 & 96.00 \\
3 & 53.00 & 28.00 & 54.00 & 78.00 \\
4 & 90.00 & 88.00 & 84.00 & 93.00 \\
5 & 100.00 & 100.00 & 100.00 & 100.00 \\
6 & 41.00 & 31.00 & 45.00 & 63.00 \\
7 & 54.00 & 80.00 & 65.00 & 82.00 \\
8 & 87.00 & 86.00 & 78.00 & 88.00 \\
9 & 100.00 & 99.00 & 100.00 & 99.00 \\ \hline
**AA** & 79.00 & 73.00 & 78.00 & 88.50 \\ \hline
**OA** & 84.59 & 71.76 & 82.24 & 90.90 \\ \hline \end{tabular}
\end{table} 
Table 14: Comparison of Class-wise Accuracy of the Proposed Technique with Existing Techniques on PU dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**MCCA-RF (\%)**} & \multirow{2}{*}{**MCCA-Naive**} & \multirow{2}{*}{**MCCA-KNN (\%)**} & \multirow{2}{*}{**Proposed**} \\  & & & **Bayes (\%)** & & **(MCCA-SVM (\%))** \\ \hline
1 & 90.00 & 70.00 & 84.00 & 90.00 \\
2 & 97.00 & 78.00 & 94.00 & 96.00 \\
3 & 53.00 & 28.00 & 54.00 & 78.00 \\
4 & 90.00 & 88.00 & 84.00 & 93.00 \\
5 & 100.00 & 100.00 & 100.00 & 100.00 \\
6 & 41.00 & 31.00 & 45.00 & 63.00 \\
7 & 54.00 & 80.00 & 65.00 & 82.00 \\
8 & 87.00 & 86.00 & 78.00 & 88.00 \\
9 & 100.00 & 99.00 & 100.00 & 99.00 \\ \hline
**AA** & 79.00 & 73.00 & 78.00 & 88.50 \\ \hline
**OA** & 84.59 & 71.76 & 82.24 & 90.90 \\ \hline \end{tabular}
\end{table} 
Table 13: Comparison of Class-wise Accuracy of the Proposed Technique with Existing Techniques on IP dataset. The segmentation maps generated after application of the Proposed Technique (MCCA-SVM) and existing techniques have been shown in Figures 12-14. The proposed MCCA-SVM gives better segmentation maps in comparison to other techniques with MCCA. In Figure 12 MCCA-SVM produces the best segmentation map of IP closest to the Ground Truth as compared to other techniques. The segmentation map of Naive Bayes produced the noisiest segmentation results with highest misclassified pixels. Similarly, for PU and SA in Figures 13 and 14, the proposed technique produces segmentation map as closest to the Ground Truth indicating more accurate semantic segmentation of the landcover. It is evident from Tables 13-15 that the Proposed Technique has performed better than existing techniques in terms of the metric 'Average Accuracy (AA)'. For IP, PU and SA dataset, Highest AA of 85.75%, 88.50% and 97.00% is given by the Proposed Technique (MCCA-SVM). Figure 14: Comparison of MCCA and different ML techniques on SA (**a**) Ground Truth, (**b**) Segmentation map after SVM, (**c**) Segmentation map after RF, (**d**) Segmentation map after Naive Bayes, (**e**) Segmentation map after KNN. Figure 13: Comparison of MCCA and different ML techniques on PU (**a**) Ground Truth, (**b**) Segmentation map after SVM, (**c**) Segmentation map after RF, (**d**) Segmentation map after Naive Bayes, (**e**) Segmentation map after KNN. 3.3 Comparison between Semantic Segmentation Performed Using the Proposed Techniques PCCA-SVM and MCCA-SVM  
Understanding HSI as a multi-view dataset and application of CCA has given better results than PCA-SVM for semantic segmentation of HSI. With the implementation of MCCA in conjunction with SVM has notably improved computational results in HSI segmentation when compared to its counterpart, PCCA. PCCA involves multiple analyses, each focusing locally on a pair of views, potentially leading to scalability issues as the number of views increases. In contrast, MCCA addresses the dimensionality challenge by jointly analyzing all views in a unified framework. MCCA excels in handling multiple sets of data simultaneously, enabling a more comprehensive exploration of spectral relationships within hyperspectral scenes.