# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 5 Experimental Results and Discussion
### Comparitive Analysis of PCA and CCA  
The analysis of hyperspectral images is very challenging due to many complexities. One such complexity is Hughes phenomena or the curse of dimensionality which when handled beforehand aids in smooth HSI analysis. As discussed earlier, PCA has been the most common technique for dimension reduction. However, it does not focus on HSI as a composition of hundreds of views or single-band datasets. PCA works on single dataset and gives reduced features, capturing maximum variance. With CCA, we have been able to reduce dimension by treating HSI in its original habitat i.e., multiple views. By applying PCCA which works locally between two views at a time, we have achieved higher OA than PCA for semantic segmentation of IP, PU and SA. The paper also implements an improved and faster version of CCA which is MCCA that works on views globally and gives higher OA on all the three datasets. The detailed comparison between PCA, PCCA and MCCA has been highlighted in Tables 1-3. Tables 1-3 bring out the better performance of CCA as compared to PCA for semantic segmentation with SVM using reduced features of IP, PU and SA datasets resp. The features reduced using the inherent multi-view nature of HSI have given a better performance than the regularly reduced features using PCA. In the earlier studies of PCA implementation for dimension reduction, researches have usually selected the value of Number of Components \(=3\). Here, the Number of Components means the number of reduced features or Principal Components being selected. For CCA, Number of Components selects the number of Canonical Variables which is a linear combination of original features. Table 1 presents the results of feature extraction with 3 number of components on the IP dataset with the highest OA of 77.02% and 85.75% for PCCA and MCCA, respectively as compared to OA 62.32% by PCA. For the same number of components on PU dataset PCA records an OA of 78.83% whereas a significant jump in OA is observed by PCCA and MCCA, giving an OA of 89.95% and 90.90 %,resp. which indicates better dense pixel prediction. The proposed technique of PCCA and MCCA records the highest OA on SA as compared to IP and PU. For 3 number of components, an OA of 92.15% and 93.88% is recorded for PCCA and MCCA, resp. Although, choosing No. of Components \(=3\) can be the criteria according to earlier researches too but for experimental and comparison point of view, the performance of PCA and CCA has been compared against a range of \(3,4,5\ldots 10\). It has shown superiority of multi-view features extraction using CCA as compared to PCA. Figures 6-8 show the resultant segmentation maps obtained from the PCA-SVM, PCCA-SVM and MCCA-SVM. These have been compared together with the Ground Truth images of the datasets. These figures show comparison of ground truth, PCA, PCCA and MCCA on IP, PU and SA, resp. where MCCA showcases the best segmentation map with lesser misclassified pixels (seen as noise in the map) followed by PCCA. It is evident from the images of better segmentation and lesser misclassified pixels using the Proposed Technique with CCA variants. \begin{table}
\begin{tabular}{c c c c} \hline
**No. of Components** & **PCA-SVM (\%)** & **PCCA-SVM (\%)** & **MCCA-SVM (\%)** \\ \hline
3 & 62.32 & 77.02 & 85.75 \\
4 & 63.25 & 73.46 & 89.41 \\
5 & 64.14 & 75.07 & 89.56 \\
6 & 64.42 & 73.31 & 88.92 \\
7 & 64.61 & 72.53 & 88.24 \\
8 & 64.87 & 71.02 & 88.09 \\
9 & 65.44 & 70.90 & 88.48 \\
10 & 65.75 & 69.25 & 87.46 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of Overall Accuracy of PCA and CCA on IP dataset. \begin{table}
\begin{tabular}{c c c c} \hline
**No. of Components** & **PCA-SVM (\%)** & **PCCA-SVM (\%)** & **MCCA-SVM (\%)** \\ \hline
3 & 78.83 & 89.95 & 90.90 \\
4 & 81.34 & 88.56 & 92.66 \\
5 & 81.98 & 88.38 & 95.07 \\
6 & 84.86 & 87.94 & 94.92 \\
7 & 86.22 & 87.45 & 94.79 \\
8 & 86.75 & 87.39 & 94.55 \\
9 & 86.83 & 86.66 & 94.50 \\
10 & 87.62 & 86.43 & 94.43 \\ \hline \end{tabular}
\end{table}