## 3 Experimental Results
### Comparison Between Different Classifiers  
In this study, five machine learning classifiers, KNN, logistic regression, decision tree, naive Bayesian model, and SVM were selected, and the established classification algorithms [14; 16], such as SRC-T (SRC classifier with diagonal weight matrix T), ELM, SVM-RBF (SVM classifier with radial basis function), SAE-LR (stacked AutoEncoders with logistic regression), and CNN, were compared with our framework. Tables 2 and 3, respectively, show the classification accuracy of different classifiers for each category for the Pavia University dataset and Kennedy Space Center dataset. The results show that compared to the other four classifiers, SVM classifier performs better in both single category and overall indicators.  
Different classifiers in machine learning have different theoretical basis, and their classification accuracy is also restricted by many external factors. For example, random forest classifier does not perform well in fitting data with many categories of features. In addition, the naive Bayes algorithm is often used in text classification, which is very sensitive to the expression form of the input data; therefore, it performs poorly in the task of HSIC. SVM classifier is different from other classification algorithms, since it does not involve probability measures and law of large numbers (LNN). The support vector plays a decisive role in the classification. Therefore, its own optimization goal is to minimize structured risk rather than experience risk, avoiding the traditional process from induction to deduction. By grabbing key samples to complete its inference process, SVM shows superior classification performance and robustness in hyperspectral imagery classification tasks. SVM is selected as the optimal classification algorithm of the framework proposed in this paper, and the classifier variables are fixed in the following comparative experiments.  
To show the superiority of the framework, a horizontal comparison experiment is needed. Tables 4 and 5, respectively, represent the differences in classification accuracy between this framework and some previous algorithms [14; 16] for the Pavia University datasets and Kennedy Space Center datasets. Within the known range, the accuracy of the algorithm in this paper has reached the state of the art.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**CLASS** & **KNN** & **Logistic Regression** & **Decision Tree** & **Naive Bayesian Model** & **SVM** \\ \hline
1 & 98.72 & 93.79 & 92.16 & 96.17 & **99.19** \\
2 & 97.81 & 98.34 & 94.98 & 98.04 & **99.48** \\
3 & 96.69 & 97.05 & 84.67 & 85.92 & **99.30** \\
4 & **98.57** & 97.07 & 81.25 & 47.66 & 97.53 \\
5 & 98.00 & **99.92** & 93.48 & 84.52 & **99.92** \\
6 & 99.39 & 98.43 & 92.06 & 86.60 & **99.79** \\
7 & 96.63 & 90.20 & 84.68 & 77.17 & **98.90** \\
8 & **98.01** & 89.37 & 82.19 & 78.53 & 96.91 \\
9 & **100.00** & 98.04 & 99.22 & **100.00** & 99.78 \\ \hline OA & 98.16 & 96.51 & 91.49 & 85.33 & **99.10** \\ AA & 98.20 & 95.80 & 89.64 & 83.85 & **98.98** \\ Kappa & 97.56 & 95.36 & 88.73 & 81.40 & **98.81** \\ \hline \end{tabular}
\end{table}
Table 2: Classification accuracy (%) for the Pavia university dataset via different classifiers we used in this paper.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**CLASS** & **KNN** & **Logistic Regression** & **Decision Tree** & **Naive Bayesian Model** & **SVM** \\ \hline
1 & 93.89 & 96.65 & 86.07 & 93.47 & **98.89** \\
2 & 80.07 & 98.65 & 48.60 & 86.59 & **100** \\
3 & 98.12 & **100** & 76.50 & 95.24 & 97.98 \\
4 & 97.02 & **100** & 52.36 & 80.75 & 99.13 \\
5 & 99.32 & **100** & 45.18 & 96.58 &