# Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Training Size** & \multicolumn{2}{c}{**5**} & \multicolumn{2}{c}{**10**} & \multicolumn{2}{c}{**30**} & \multicolumn{2}{c}{**50**} \\ \hline Classifier & NN & SVM & NN & SVM & NN & SVM & NN & SVM \\ GlobalLPP-SF & 44.40 & 49.11 & 49.47 & 56.26 & 58.52 & 67.90 & 62.67 & 72.96 \\ GlobalLPP-SSCF & 62.81 & 63.65 & 68.67 & 68.87 & 77.66 & 77.99 & 80.39 & 80.98 \\ SuperLPP-SF & 63.74 & 64.10 & 70.73 & 71.65 & 80.92 & 80.33 & 84.03 & 83.14 \\ SuperLPP-SSCF & 73.50 & 74.37 & 82.23 & 81.35 & 90.43 & 87.91 & 92.28 & 89.38 \\ MSuperLPP & 76.49 & 75.91 & 85.53 & 83.60 & 94.55 & 93.10 & 97.02 & 95.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The classification accuracy (%) for the Indian Pines data set with the nearest neighbor and SVM classifiers. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Training Size** & \multicolumn{2}{c}{**5**} & \multicolumn{2}{c}{**10**} & \multicolumn{2}{c}{**30**} & \multicolumn{2}{c}{**50**} \\ \hline Classifier & NN & SVM & NN & SVM & NN & SVM & NN & SVM \\ GlobalLPP-SF & 64.26 & 75.81 & 71.29 & 81.64 & 77.18 & 84.84 & 79.34 & 86.21 \\ GlobalLPP-SSCF & 75.12 & 80.18 & 81.06 & 84.92 & 85.77 & 88.50 & 86.63 & 89.19 \\ SuperLPP-SF & 70.06 & 80.65 & 75.56 & 86.88 & 81.30 & 89.48 & 83.44 & 90.55 \\ SuperLPP-SSCF & 80.81 & 84.34 & 84.79 & 87.28 & 88.00 & 90.83 & 89.22 & 92.26 \\ MSuperLPP & 82.27 & 86.64 & 90.10 & 90.68 & 93.83 & 93.75 & 94.42 & 94.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The classification accuracy (%) for the Zaoyuan data set with the nearest neighbor and SVM classifiers. Figure 2: Classification accuracy versus training size. (**a**) Indian Pines with the nearest neighbor classifier. (**b**) Indian Pines with support vector machine (SVM) classifier. (**c**) Zaoyuan with nearest neighbor classifier. (**d**) Zaoyuan with SVM classifier. (**e**) Salinas with nearest neighbor classifier. (**f**) Salinas with SVM classifier. Figure 4: Classification maps obtained with the Zaoyuan data set. (**a**) Ground truth. (**b**) GlobalLPP-SF. (**c**) GlobalLPP-SSCF. (**d**) SuperLPP-SF. (**e**) SuperLPP-SSCF. (**f**) MSuperLPP. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Training Size** & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**3**} & \multicolumn{2}{c}{**5**} & \multicolumn{2}{c}{**10**} \\ \hline Classifier & NN & SVM & NN & SVM & NN & SVM & NN & SVM \\ GlobalLPP-SF & 69.00 & 75.61 & 76.02 & 81.95 & 79.04 & 85.00 & 83.05 & 89.07 \\ GlobalLPP-SSCF & 78.69 & 80.60 & 85.14 & 86.55 & 87.11 & 88.69 & 89.97 & 91.91 \\ SuperLPP-SF & 80.67 & 80.51 & 89.82 & 89.36 & 90.74 & 92.06 & 91.95 & 93.32 \\ SuperLPP-SSCF & 81.77 & 84.23 & 90.92 & 92.37 & 91.97 & 93.28 & 93.82 & 94.50 \\ MSuperLPP & 90.46 & 88.32 & 95.55 & 94.73 & 96.87 & 96.89 & 97.78 & 97.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy (%) for the Salinas data set with nearest neighbor and SVM classifiers. Figure 3: Classification maps obtained with the Indian Pines data set. (**a**) Ground truth. (**b**) GlobalLPP-SF. (**c**) GlobalLPP-SSCF. (**d**) SuperLPP-SF. (**e**) SuperLPP-SSCF. (**f**) MSuperLPP. Therefore, it is verified by the experimental results that our MSuperLPP, involving the multiscale superpixel segmentation strategy, is able to achieve an excellent classification performance.