## 4 Experimental Results and Analysis
### Results and Discussion
While for the IND PINE dataset, compared to the truncated one in Group I, the algorithm gets poorer performance on the whole IND PINE dataset than on the truncated one. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **GK-Graph** & **LLR-Graph** & **LE-Graph** & **NNLRS-Graph** & **SR-Graph** & **PCSSR-Graph** & **CASD Assisted PCSSR** \\ \hline
1 & 87.90 & 89.60 & 96.10 & 91.10 & 91.10 & 97.20 & **99.27** \\
2 & 88.60 & 88.60 & 90.00 & 64.20 & 87.20 & 92.20 & **100.00** \\
3 & 75.30 & 76.80 & 64.40 & 53.40 & 76.50 & 74.00 & **99.96** \\
4 & 51.30 & 54.50 & 42.90 & 39.50 & 53.70 & 51.10 & **100.00** \\
5 & 56.20 & 61.40 & 40.80 & 63.50 & 54.40 & 57.10 & **100.00** \\
6 & 35.10 & 39.20 & 41.10 & 58.00 & 47.70 & 54.70 & **99.27** \\
7 & 55.70 & 56.10 & 63.20 & 54.80 & 58.50 & 69.40 & **100.00** \\
8 & 84.10 & 83.60 & 81.00 & 95.40 & 82.10 & 84.10 & **99.96** \\
9 & 89.20 & 90.40 & 91.30 & 85.60 & 91.50 & 91.50 & **100.00** \\
10 & **100.00** & **100.00** & 99.50 & 94.30 & **100.00** & **100.00** & **100.00** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy of each class, OA, AA and Kappa coefficients for KSC data with 13 classes (20 training samples for each class). The highest value of each row is shown in bold. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **GK-Graph** & **LLR-Graph** & **LE-Graph** & **NNLRS-Graph** & **SR-Graph** & **PCSSR-Graph** & **CASD Assisted PCSSR** \\ \hline
1 & 99.30 & 99.30 & **100.00** & 98.60 & **100.00** & **100.00** & **100.00** \\
2 & 99.00 & 99.00 & 97.60 & **100.00** & 99.00 & 99.50 & 99.93 \\
3 & 95.60 & 96.60 & 96.40 & 97.50 & 94.10 & 98.00 & **99.93** \\
4 & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** \\
5 & 93.00 & 96.10 & 99.30 & 95.37 & 95.50 & 98.70 & **100.00** \\
6 & 78.90 & 78.30 & 86.90 & 91.70 & 80.80 & 87.00 & **100.00** \\
7 & 97.90 & 97.90 & 94.50 & 95.20 & 97.20 & 95.90 & **99.79** \\
8 & 91.80 & 90.90 & 86.70 & 80.60 & 91.00 & 86.00 & **100.00** \\
9 & 84.70 & 84.50 & 86.50 & 92.60 & 87.10 & 95.70 & **99.79** \\ OA & 93.36 & 93.57 & 94.64 & 95.21 & 93.86 & 95.86 & **99.71** \\ AA & 93.36 & 93.62 & 94.21 & 94.62 & 93.86 & 95.64 & **99.94** \\ Kappa & 92.47 & 92.72 & 93.93 & 94.58 & 93.04 & 95.31 & **99.68** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy of each class, OA, average accuracy (AA) and Kappa coefficients for BOT data with nine classes (20 training samples for each class). The highest value of each row is shown in bold. Conclusion 1 states that the performance of the CASD assisted PCSSR algorithm is highly related to the number of labeled samples for each class. Lack of labeled samples leads to low accuracy and the increment of labeled samples can improve the result effectively. Since the result of the PCSSR algorithm is regularized by the probabilistic class structure which is generated by our CASD algorithm, the distances (CASDs) between samples have a great effect on the final performance of our algorithm. We can do the following operations to visualize the effect of the distances: for every unlabeled sample, find out the labeled sample with the shortest CASD to it, then mark that unlabeled sample. The classification results on BOT dataset (with three labeled samples per class) are shown in Figure 8. Please notice that \"the visualization of the CASD\" is an independent process, which is only for a better understanding of how well the CASD is measured. It is not an intermediate result of CASD assisted PCSSR algorithm. It is easy to see, if the labeled samples we select from different categories are very limited, they can't be sufficiently assigned to every ground block in testing set. During the classification of such a sample block, if the samples of the same category are far away or the samples of the different classes are nearby, misclassification is likely to happen.