# Hyperspectral Image Classification with Imbalanced Data Based on Semi-Supervised Learning
## 4 Results  
### Comparison of Classification Results  
In this experiment, we compared the accuracy of classification of four preprocessing methods, namely, NearMiss, RUS, SMOTE, and NearPseudo, by recording their respective accuracies before and after processing. We mainly discuss four specific issues: (1) the accuracy of classification of the aforementioned imbalanced preprocessing methods; (2) whether the differences between NearPseudo and others are statistically significant; (3) the enhancement in the accuracy of minority classification after preprocessing, and (4) in the case of training subsets with different degrees of imbalance, whether the different preprocessing methods remain consistent in terms of improving the accuracy. The results are presented in Tables 2-4. The final results include the standard deviation values and average of the five experiments. Table 2 lists the classification results of the different methods on training subset **T1**, which is an extremely imbalanced training subset with R (Max/Min) = 14.0 (Table 1). We can observe that the performance and accuracy of the classifier increased after preprocessing. The highest AF and OA were achieved by NearPseudo (AF = 74.8 \(\pm\) 0.3% and OA = 74.8 \(\pm\) 0.1%), followed by SMOTE (AF = 72.7 \(\pm\) 0.4% and OA = 72.7 \(\pm\) 0.2%), RUS (AF = 65.6 \(\pm\) 0.5% and OA = 65.8 \(\pm\) 0.1%), and NearMiss (AF = 29.6 \(\pm\) 0.6% and OA = 32.9 \(\pm\) 0.2%). Compared with the unbalanced data, NearPseudo improved both AF and OA by 2.5%, respectively, whereas SMOTE improved both AF and OA by 0.4%. The RUS and NearMiss method exhibited low classification performance. The significant result from the paired \(t\)-test shows that the AF of NearPseudo was significantly higher than that of NearMiss by 45.2% (\(p\leq\) 0.01), significantly higher than that of RUS by 9.2% (\(p\leq\) 0.01), and higher than that of SMOTE by 2.1% (\(p\leq\) 0.05). As shown in Table 2, after preprocessing by NearPseudo, the F1 scores of nine classes (75%) were improved by varying degrees. Only three classes (25%) obtained the highest F1 value when SMOTE was used to balance the training data, whereas the F1 values of NearMiss and RUS for  
each species decreased. Notably, the F1 of \(Populus\) (NO. 10), which is a minority class with 800 training samples, increased by nearly 13.2% after preprocessing through NearPseudo. Moreover, NearPseudo exhibited outstanding performance on _Oryza sativa_ (NO. 3) and _Zea mays_ (NO. 6), which are minority classes and consist of only 200 training samples. The F1 scores of _Oryza sativa_ and _Zea mays_ improved by approximately 2.0% and 3.1%, respectively. Table 3 presents the classification results of different balanced methods on the sub-training set **T2**, with R (Max/Min) = 3.5 (Table 1), which is a relatively imbalanced training subset. These results are similar to those in Table 2. It can be observed that the performance and accuracy of the classifier increased after preprocessing. The best classification performance was achieved after processing by NearPseudo (AF = 75.0 \(\pm\) 0.4% and OA = 75.1 \(\pm\) 0.1%), followed by SMOTE (AF = 73.2 \(\pm\) 0.4% and OA = 73.1 \(\pm\) 0.2%), RUS (AF = 72.8 \(\pm\) 0.4% and OA = 72.9 \(\pm\) 0.2%), and NearMiss (AF = 60.9 \(\pm\) 0.5% and OA = 60.9 \(\pm\) 0.1%). Compared to the imbalanced dataset, the classification accuracy was improved by 1.8% 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 14.1% (\(p\leq 0.001\)), significantly higher than that of RUS by 2.2% (\(p\leq 0.01\)), and significantly higher than that of SMOTE by 1.8% (\(p\leq 0.01\)). In addition, the F1 score of