# Hyperspectral Image Classification with Imbalanced Data Based on Semi-Supervised Learning
## 4 Results
### Impact of Different Hyperparameters  
NearPseudo has two hyperparameters: number of random subset samples \(q\) and the number of nearest neighbor samples obtained for each iteration \(k\). The two hyperparameters were selected manually. In this experiment, for NearPseudo, we adopted different values of \(q\) = [5000, 10,000, 15,000, 20,000, 25,000, 30,000] and \(k\) = [2, 3, 4, 5, 6, 7, 8] (Figure 3). We set different hyperparameters of NearPseudo and performed classification experiments on training subsets **T1**, **T2**, and **T3** (Table 1). This helped evaluate the impact of the two hyperparameters on the classification accuracy in NearPseudo and further reveal the mechanism underlying the changes in hyperparameters to guide their manual setting in NearPseudo.  
All three imbalanced training subsets exhibited a decrease in the OA curve of the classifier with the hyperparameter \(k\) of NearPseudo (Figure 3a-c). Specifically, when the parameter \(k\) was varied from 2 to 8, the classification OA based on **T1** decreased by 1.1% from 74.8% to 73.7% (Figure 3a). Under the same conditions, the classification OA based on **T2** decreased by 0.8% from 75.1% to 74.2% (Figure 3b), whereas the classification OA based on **T3** decreased by 0.6% from 76.5% to 75.9% (Figure 3c). Thus, it can be deduced that the improvement in the accuracy of the classifier by NearPseudo is affected by the hyperparameter \(k\). The larger the hyperparameter \(k\), the worse the accuracy. The intuitive explanation of this is included in Section 5.2.  
In contrast to hyperparameter \(k\), all results of using three imbalanced training subsets showed that the OA curve of the classifier increased with the hyperparameter \(q\) (Figure 3d-f). Specifically, when the parameter \(q\) was varied from 5000 to 30,000, the classification OA based on **T1** increased by 0.7% from 73.4% to 74.1% (Figure 3d). Under the same conditions, the classification OA based on **T2** increased by 0.7% from 73.8% to 74.5% (Figure 3e), whereas the classification OA based on **T3** increased by 0.4% from 75.8% to 75.9% (Figure 3f).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**CLA.**} & \multicolumn{4}{c}{**Unbalanced**} & \multicolumn{4}{c}{**NearPseudo**} \\ \cline{2-9}  & **CART** & \(k\)**NN** & **LR** & **RF** & **CART** & \(k\)**NN** & **LR** & **RF** \\ \hline
1 & 45.0\% & 62.0\% & 52.5\% & 69.8\% & 48.7\% & 64.2\% & 54.3\% & 70.1\% \\
2 & 45.4\% & 60.5\% & 63.4\% & 69.1\% & 49.2\% & 62.7\% & 64.9\% & 69.7\% \\
3 & 90.9\% & 93.0\% & 95.2\% & 94.6\% & 89.9\% & 93.4\% & 95.4\% & 95.3\% \\
4 & 39.3\% & 50.1\% & 49.9\% & 60.8\% & 41.0\% & 52.3\% & 48.2\% & 62.9\% \\
5 & 54.2\% & 66.2\% & 67.7\% & 73.2\% & 59.7\% & 68.6\% & 71.0\% & 75.3\% \\
6 & 90.2\% & 92.3\% & 93.0\% & 94.8\% & 91.0\% & 93.7\% & 93.2\% & 95.7\% \\
7 & 88.6\% & 91.1\% & 91.0\% & 92.9\% & 89.9\% & 92.0\% & 92.5\% & 93.7\% \\
8 & 47.0\% & 54.6\% & 45.7\% & 71.5\% & 58.9\% & 65.6\% & 65.7\% & 73.9\% \\
9 & 39.9\% & 46.7\% & 43.9\% & 53.7\% & 37.6\% & 46.9\% & 50.4\% & 53.6\% \\
10 & 44.4\% & 55.8\% & 68.2\% & 67.0\% & 52.0\% & 64.3\% & 68.5\% & 69.5\% \\
11 & 55.5\% & 60.9\% & 56.6\% & 70.3\% & 56.8\% & 64.4\% & 59.4\% & 70.0\% \\
12 & 46.4\% & 57.9\% & 8.0\% & 63.5\% & 57.5\% & 68.5\% & 66.6\% & 70.4\% \\ AF & 57.2\% & 65.9\% & 61.3\% & 73.4\% & 61.0\% & 69.7\% & 69.2\% & 75.0\% \\ OA & 57.0\% & 65.8\% & 63.1\% & 73.3\% & 60.7\% & 69.8\% & 69.5\% & 75.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification accuracy (%) of different solution.  
76.2% (Figure 3f). Therefore, improvement in the accuracy of the classifier by NearPseudo is affected by the hyperparameter \(q\); the larger the hyperparameter \(q\), the better the accuracy. The intuitive explanation of this is included in Section 5.2.