# Hyperspectral Image Classification with Imbalanced Data Based on Semi-Supervised Learning
## 4 Results
### Comparison of Classification Results
\begin{table}
\begin{tabular}{c c c c c c} \hline
**CLA.** & **Unbalanced** & **NearMiss** & **RUS** & **SMOTE** & **NearPseudo** \\ \hline
1 & 66.3 \(\pm\) 0.3 & 20.6 \(\pm\) 0.4 & 58.4 \(\pm\) 0.5 & 66.7 \(\pm\) 0.4 & 68.9 \(\pm\) 0.3 \\
2 & 68.0 \(\pm\) 0.4 & 18.6 \(\pm\) 0.4 & 57.2 \(\pm\) 0.4 & 68.7 \(\pm\) 0.3 & 71.4 \(\pm\) 0.5 \\
3 & 92.0 \(\pm\) 0.2 & 50.5 \(\pm\) 0.4 & 91.1 \(\pm\) 0.2 & 92.8 \(\pm\) 0.3 & 94.0 \(\pm\) 0.2 \\
4 & 59.2 \(\pm\) 0.4 & 11.3 \(\pm\) 0.7 & 44.2 \(\pm\) 1.0 & 60.0 \(\pm\) 0.6 & 62.7 \(\pm\) 0.4 \\
5 & 74.9 \(\pm\) 0.5 & 21.6 \(\pm\) 1.4 & 66.1 \(\pm\) 0.4 & 75.0 \(\pm\) 0.7 & 78.0 \(\pm\) 0.2 \\
6 & 89.3 \(\pm\) 0.1 & 89.3 \(\pm\) 0.1 & 92.7 \(\pm\) 0.2 & 90.4 \(\pm\) 0.2 & 92.4 \(\pm\) 0.1 \\
7 & 92.4 \(\pm\) 0.1 & 28.6 \(\pm\) 0.3 & 90.8 \(\pm\) 0.3 & 92.1 \(\pm\) 0.1 & 92.4 \(\pm\) 0.1 \\
8 & 74.3 \(\pm\) 0.3 & 29.6 \(\pm\) 0.6 & 64.2 \(\pm\) 0.5 & 73.5 \(\pm\) 0.6 & 73.4 \(\pm\) 0.3 \\
9 & 52.7 \(\pm\) 0.3 & 9.3 \(\pm\) 0.5 & 44.0 \(\pm\) 0.5 & 53.2 \(\pm\) 0.3 & 53.6 \(\pm\) 0.5 \\
10 & 54.8 \(\pm\) 0.4 & 24.7 \(\pm\) 0.1 & 53.9 \(\pm\) 0.9 & 55.4 \(\pm\) 0.4 & 68.0 \(\pm\) 0.7 \\
11 & 68.6 \(\pm\) 0.1 & 20.9 \(\pm\) 1.5 & 61.4 \(\pm\) 0.6 & 68.9 \(\pm\) 0.5 & 68.2 \(\pm\) 0.2 \\
12 & 74.8 \(\pm\) 0.4 & 30.3 \(\pm\) 0.2 & 63.2 \(\pm\) 0.7 & 75.3 \(\pm\) 0.3 & 75.2 \(\pm\) 0.5 \\ AF & 72.3 \(\pm\) 0.3 & 29.6 \(\pm\) 0.6 & 65.6 \(\pm\) 0.5 & 72.7 \(\pm\) 0.4 & 74.8 \(\pm\) 0.3 \\ OA & 72.3 \(\pm\) 0.1 & 32.9 \(\pm\) 0.2 & 65.8 \(\pm\) 0.1 & 72.7 \(\pm\) 0.2 & 74.8 \(\pm\) 0.1 \\ \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy (%) of different solutions on sub-training data **T1**. \begin{table}
\begin{tabular}{c c c c c c} \hline
**CLA. ** & **Unbalanced** & **NearMiss** & **RUS** & **SMOTE** & **NearPseudo** \\ \hline
1 & 69.8 \(\pm\) 0.6 & 53.5 \(\pm\) 0.3 & 66.1 \(\pm\) 0.5 & 69.7 \(\pm\) 0.5 & 70.1 \(\pm\) 0.6 \\
2 & 69.1 \(\pm\) 0.5 & 55.4 \(\pm\) 0.7 & 65.6 \(\pm\) 0.4 & 67.7 \(\pm\) 0.4 & 69.7 \(\pm\) 0.7 \\
3 & 94.6 \(\pm\) 0.1 & 79.8 \(\pm\) 0.8 & 92.9 \(\pm\) 0.2 & 94.4 \(\pm\) 0.2 & 95.3 \(\pm\) 0.2 \\
4 & 60.8 \(\pm\) 0.5 & 43.0 \(\pm\) 0.4 & 56.7 \(\pm\) 0.8 & 60.7 \(\pm\) 0.6 & 62.9 \(\pm\) 0.5 \\
5 & 73.2 \(\pm\) 0.4 & 65.6 \(\pm\) 0.4 & 72.7 \(\pm\) 0.5 & 72.6 \(\pm\) 0.7 & 75.3 \(\pm\) 0.4 \\
6 & 94.8 \(\pm\) 0.2 & 89.5 \(\pm\) 0.3 & 94.9 \(\pm\) 0.1 & 94.7 \(\pm\) 0.1 & 95.7 \(\pm\) 0.2 \\
7 & 92.9 \(\pm\) 0.1 & 74.3 \(\pm\) 0.8 & 93.1 \(\pm\) 0.2 & 92.9 \(\pm\) 0.2 & 93.7 \(\pm\) 0.2 \\
8 & 71.5 \(\pm\) 0.3 & 49.9 \(\pm\) 0.4 & 72.5 \(\pm\) 0.4 & 71.2 \(\pm\) 0.3 & 73.9 \(\pm\) 0.3 \\
9 & 53.7 \(\pm\) 0.3 & 37.3 \(\pm\) 0.6 & 49.7 \(\pm\) 0.5 & 54.5 \(\pm\) 0.5 & 53.6 \(\pm\) 0.5 \\
10 & 67.0 \(\pm\) 0.4 & 58.0 \(\pm\) 0.3 & 67.1 \(\pm\) 0.6 & 65.7 \(\pm\) 0.3 & 69.5 \(\pm\) 0.5 \\
11 & 70.3 \(\pm\) 0.2 & 59.0 \(\pm\) 0.5 & 68.7 \(\pm\) 0.5 & 69.8 \(\pm\) 0.4 & 70.0 \(\pm\) 0.2 \\
12 & 63.5 \(\pm\) 0.4 & 65.5 \(\pm\) 0.5 & 73.0 \(\pm\) 0.3 & 64.3 \(\pm\) 0.9 & 70.4 \(\pm\) 0.7 \\ AF & 73.4 \(\pm\) 0.3 & 60.9 \(\pm\) 0.5 & 72.8 \(\pm\) 0.4 & 73.2 \(\pm\) 0.4 & 75.0 \(\pm\) 0.4 \\ OA & 73.3 \(\pm\) 0.1 & 60 the 10 classes was improved by 83.33%, and the best accuracy was achieved by NearPseudo. Similarly, the accuracy of these minority classes can be improved by using NearPseudo to various degrees. For example, it can be observed in Table 3 that the classifier exhibited outstanding performance on the _Amygdala_ (NO. 12) and _Zea mays_ (NO. 8) classes. These two classes are minority classes and have the fewest samples (only 800 training samples). The F1 score of _Amygdala_ improved by 6.9%, which is the greatest improvement compared with the other classes. Furthermore, the F1 score of _Zea mays_ improved by 2.4%. Table 4 presents the classification results on sub-training set **T3**, with R (Max/Min) = 1.6 (Table 1). The results are similar to those in Tables 2 and 3.