# Hyperspectral Image Classification with Imbalanced Data Based on Semi-Supervised Learning
## 4 Results
### Comparison of Classification Results
They show that the best classification performance was achieved by NearPseudo (AF = 76.3 \(\pm\) 0.4% and OA = 76.5 \(\pm\) 0.1%), followed by RUS (AF = 76.1 \(\pm\) 0.4% and OA = 76.2 \(\pm\) 0.2%), SMOTE (AF = 75.7 \(\pm\) 0.4% and OA = 76.0 \(\pm\) 0.2%), and NearMiss (AF = 72.5 \(\pm\) 0.4% and OA = 72.5 \(\pm\) 0.1%). Compared to the imbalanced dataset, the classification accuracy improved by 0.4% after being balanced by NearPseudo. The significant result from the paired _t_-test shows that the AF of NearPseudo was extremely significantly higher than that of NearMiss by 3.8% (\(p\leq 0.001\)) and significantly higher than that of SMOTE by 0.6% (\(p\leq 0.01\)). The difference was not significant between the AF of NearPseudo and that of RUS (\(p=0.27\)). The F1 score of 9 classes (75%) improved, and the best performance was exhibited by NearPseudo. It can be observed from Table 4 that the F1 score of three minority classes, _Sophora japonica_ (NO. 4), _Pyrus_ (NO. 9), and _Amygdala_ (NO. 12), increased by 1.9%, 0.7%, and 0.3%, respectively, thereby verifying the performance of NearPseudo. Figure 2 depicts the discrepancy in the accuracy of the preprocessing methods in the case of training subsets with different degrees of imbalance. After balancing with NearPseudo, the accuracies of the extremely imbalanced sub-training set **T1**, relatively imbalanced sub-training set **T2**, and slightly imbalanced sub-training set **T3** improved by 2.6%, 1.9%, and 0.4%, respectively. However, after balancing with SMOTE, the accuracies of **T1**, **T2**, and **T3** improved by 0.4%, \(-0.2\%\), and \(-0.1\%\), respectively. With an increase in the degree of imbalance of the dataset, the classification accuracy of the two processing methods increased. This change was most prominent in the case of NearPseudo. The results indicate that the greater the imbalance in the sub-training data, the higher the improvement in the accuracy of the imbalanced data after being balanced by NearPseudo. Tables 2-4 show that the accuracy of most minority classes can be improved by NearPseudo. In sub-training set **T1**, the average accuracy of minority classes with minimum sample size (NO. 3 and NO. 6) was improved by 2.6%. In sub-training set **T2**, the average accuracy of minority classes with minimum sample size (NO. 8 and NO. 12) was improved \begin{table}
\begin{tabular}{c c c c c c} \hline
**CLA.** & **Unbalanced** & **NearMiss** & **RUS** & **SMOTE** & **NearPseudo** \\ \hline
1 & 70.0 \(\pm\) 0.3 & 63.2 \(\pm\) 0.5 & 70.0 \(\pm\) 0.4 & 69.8 \(\pm\) 0.1 & 71.4 \(\pm\) 0.4 \\
2 & 72.3 \(\pm\) 0.5 & 69.5 \(\pm\) 0.4 & 72.0 \(\pm\) 0.4 & 71.9 \(\pm\) 0.4 & 72.2 \(\pm\) 0.5 \\
3 & 95.2 \(\pm\) 0.2 & 92.0 \(\pm\) 0.3 & 95.3 \(\pm\) 0.2 & 95.3 \(\pm\) 0.1 & 95.5 \(\pm\) 0.3 \\
4 & 60.1 \(\pm\) 0.6 & 58.4 \(\pm\) 0.4 & 61.6 \(\pm\) 0.6 & 60.3 \(\pm\) 0.3 & 62.0 \(\pm\) 0.3 \\
5 & 76.8 \(\pm\) 0.4 & 72.7 \(\pm\) 0.4 & 77.7 \(\pm\) 0.5 & 76.7 \(\pm\) 0.4 & 77.5 \(\pm\) 0.3 \\
6 & 95.7 \(\pm\) 0.2 & 93.9 \(\pm\) 0.2 & 95.7 \(\pm\) 0.2 & 95.1 \(\pm\) 0.1 & 95.7 \(\pm\) 0.2 \\
7 & 93.9 \(\pm\) 0.1 & 90.5 \(\pm\) 0.3 & 93.4 \(\pm\) 0.1 & 93.8 \(\pm\) 0.2 & 94.0 \(\pm\) 0.2 \\
8 & 77.2 \(\pm\) 0.5 & 73.1 \(\pm\) 0.6 & 76.5 \(\pm\) 0.4 & 77.0 \(\pm\) 0.3 & 77.0 \(\pm\) 0.4 \\
9 & 52.7 \(\pm\) 1.1 & 50.5 \(\pm\) 0.6 & 54.1 \(\pm\) 0.5 & 52.6 \(\pm\) 1.0 & 53.4 \(\pm\) 0.7 \\
10 & 71.9 \(\pm\) 0.3 & 66.7 \(\pm\) 0.4 & 71.6 \(\pm\) 0.4 & 71.8 \(\pm\) 0.5 & 71.8 \(\pm\) 0.6 \\
11 & 69.4 \(\pm\) 0.2 & 64.1 \(\pm\) 0.2 & 69.3 \(\pm\) 0.3 & 69.1 \(\pm\) 0.6 & 70.5 \(\pm\) 0.3 \\
12 & 74.5 \(\pm\) 0.3 & 74.8 \(\pm\) 0.2 & 75.8 \(\pm\) 0.6 & 74.7 \(\pm\) 0.6 & 74.8 \(\pm\) 0.4 \\ AF & 75.8 \(\pm\) 0.4 & 72.5 \(\pm\) 0.4 & 76.1 \(\pm\) 0.4 & 75.7 \(\pm\) 0.4 & 76.3 \(\pm\) 0.4 \\ OA & 76.1 \(\pm\) 0.1 & 72.5 \(\pm\) 0.1 & 76.2 \(\pm\) 0.2 & 76.0 \(\pm\) 0.2 & 76.5 \(\pm\) 0.1 \\ \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy (%) of different solutions on sub-training data **T3**. by 4.7%.