## 4 Experimental Results and Analysis
### Results and Discussion  
Figure 6 shows how the classification overall accuracy (OA) changes with the number of labeled samples on six different datasets, and Figure 7 demonstrates the visualized classification results. For the classification result on the KSC dataset, as illustrated in Figure 6, the CASD assisted PCSSR-graph method performs better than other methods when the number of labeled samples is more than 5 per class, finally achieving an accuracy about 97% and about 10% higher than other methods. For the result on the BOT dataset, as illustrated in Figure 6, the CASD assisted PCSSR-graph method performs better than other methods when the number of labeled samples is more than 5 per class, finally achieving an accuracy about 99% and about 5% higher than other methods. For the result on the truncated IND PINE dataset presented in Figure 6, the performance of our method surpasses other methods all along, and obtains an accuracy about 96% and about 16% higher than other methods when the number of labeled samples is 15 per class. Figure 5: An example of dividing IND PINE dataset into training set and testing set. (**a**) The complete IND PINE dataset. (**b**) Training set generated by randomly picking out 10 samples per class from the complete IND PINE dataset. (**c**) Testing set. Figure 6: Overall accuracy with different number of labeled samples on all datasets. (**a**) KSC data with 13 classes (\(\lambda_{1}=1\times 10^{-4},\lambda_{2}=2\times 10^{-5}\)). (**b**) BOT data with 9 classes (\(\lambda_{1}=1\times 10^{-4},\lambda_{2}=2\times 10^{-6}\) ). (**c**) Truncated IND PINE data with 16 classes (\(\lambda_{1}=1\times 10^{-4},\lambda_{2}=2\times 10^{-5}\) ). (**d**) IND PINE data with 16 classes (\(\lambda_{1}=1\times 10^{-4},\lambda_{2}=4\times 10^{-5}\) ). (**e**) SAL data with 16 classes (\(\lambda_{1}=1\times 10^{-4},\lambda_{2}=6\times 10^{-6}\) ). (**f**) Pavia University (PAV) data with 9 classes (\(\lambda_{1}=1\times 10^{-4},\lambda_{2}=4\times 10^{-6}\) ). Furthermore, the classification accuracy of each class, the overall accuracy (OA), the average accuracy (AA), and the Kappa coefficient for the different graph-based methods on three datasets are shown in Tables 3-5, where the highest value of each row is shown in bold. For the BOT dataset, Table 3 exhibits that our method outperforms all the other algorithms with the best class-specific accuracies on almost all indices on all classes. The only exception is that on Class Two, our method achieves an accuracy 99.93% whereas the highest accuracy is 100.00%. For the KSC dataset, Table 4 presents that our method achieves better performance than all the other algorithms on almost all indices. The only exception is that on Class 11 our method achieves an accuracy 99.64% whereas the highest accuracy is 99.70%. For the truncated IND PINE dataset, Table 5 shows that our method outperforms all the other algorithms once again with the best class-specific accuracies on almost all indices. The only exception is that on Class Eight our method achieves an accuracy 99.64% whereas the highest accuracy is 100.00%. All the above figures and tables clarify that the classification accuracies of our model are more satisfactory than other traditional graph-based methods. Based on the above experiment results, we can come to the following conclusions:  
1. For datasets in Group I, the CASD assisted PCSSR algorithm doesn't perform so well when a small number of labeled samples are provided. However, as more labeled samples are given, our method gradually surpasses other graph-based methods, finally by more than 5% in overall accuracy. The experiment result indicates the introduction of the spatial information can effectively improve the classification accuracy of those traditional spectral-focusing algorithms when given a relatively larger training set. 2. For datasets in Group II, our algorithm achieves super high accuracy on the SAL dataset.