# Hyperspectral Image Classification with Imbalanced Data Based on Semi-Supervised Learning
## 6 Conclusions  
The most common classifiers have a weak ability to deal with imbalanced data problems. They often provide prejudicial and biased classification results when an imbalanced dataset is used. In this paper, a novel method based on semi-supervised learning called NearPseudo is proposed to solve this problem. This solution makes full use of abundant information and features of unlabeled remote sensing images, balances imbalanced data,and improves the classification accuracy at a low cost. Experimental evaluation using a hyperspectral dataset from Xiongan proved the effectiveness of this method. The experiments were designed to analyze the following issues: (1) performance comparison of NearPseudo with other popular preprocessing methods in different imbalanced environments, (2) the effect of NearPseudo on the classification accuracy of these common classifiers, and (3) the mechanism of change in the hyperparameters of NearPseudo. The experimental results demonstrate the simplicity and universality of NearPseudo. The major conclusions of this study are as follows:  
1. Compared with other methods, NearPseudo exhibits enhanced efficiency and superiority in balancing data with semi-supervised learning. In addition, NearPseudo is more suitable for extremely imbalanced datasets. After using NearPseudo, the RF accuracies of the sub-training set **T1**, **T2**, and **T3** were improved by 2.6%, 1.9%, and 0.4%, respectively.
2. The classification performance of most minority classes can be improved by using NearPseudo. After using NearPseudo, the average accuracy of minority classes with fewest samples in **T1**, **T2**, and **T3** were improved by 2.6%, 4.7%, and 1.0%, respectively.
3. The classification performance of most multi-classifiers with imbalanced data can be improved by using NearPseudo. After using NearPseudo, the accuracy of RF, \(k\)NN, LR, and CART increased by 1.8%, 4.0%, 6.4% and 3.7% in sub-training sets **T2**, respectively.
4. The performance of NearPseudo increases with hyperparameter \(q\) and decreases with hyperparameter \(k\).  
In future research, we intend to further modify NearPseudo by improving its search efficiency and introducing ensemble learning to improve the classification performance.  
Conceptualization, X.Z. and J.C.; Data curation, X.Z. and J.J.; Formal analysis, X.Z. and J.J.; Funding acquisition, J.C. and L.S.; Investigation, X.Z. and J.J.; Methodology, X.Z. and J.C.; Project administration, J.C.; Resources, J.C. and S.G.; Software, X.Z. and [PERSON]; Supervision, [PERSON] and [PERSON]; Validation, [PERSON], [PERSON] and [PERSON]; Visualization, [PERSON]; Writing--original draft, [PERSON] and [PERSON]; Writing--review & editing, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant XDA19030301, and in part by Fundamental Research Foundation of Shenzhen Technology and Innovation Council under Grant KCXFZ202002011006298, in part by the National Natural Science Foundation of China Project under Grant 42001286 and under Grant 42171323, in part by Huawei Project (No. 9424877), in part by Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515011501).  
Not applicable.  
The authors would like to thank reviewers for suggestions and [PERSON] from SIAT for valuable suggestions.  
The authors declare no conflict of interest.  
The following notations and abbreviation are used in this manuscript:  
\begin{tabular}{l l} \(x\) & Scalars \\ \(\mathbf{x}\) & Vectors \\ \(\mathbf{T_{r}}\) & Training dataset \\ \(\mathbf{T_{u}}\) & Unlabeled dataset \\ \(n\) & The number of total samples for \(\mathbf{T_{r}}\) \\ \(n^{\prime}\) & The number of supplement samples \\ \(v\) & The number of bands/features \\ \(m\) & The number of categorizations \\ \(z\) & The number of total samples for \(\mathbf{T_{u}}\) \\ \end{tabular}