## 4 Experimental Results and Analysis
### Results and Discussion
The flaws in the probabilistic class structure generated by spatial algorithm can interfere the following sparse representation process, finally resulting in a decrease of accuracy. With the number of labeled samples increasing, the probability that a block is assigned to labeled samples will rise, the accuracy of the algorithm will be improved, and finally, the OA will be improved. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **GK-Graph** & **LLR-Graph** & **LLE-Graph** & **NNLRS-Graph** & **SR-Graph** & **PCSSR-Graph** & **CASD Assisted PCSSR** \\ \hline
11 & 99.10 & 99.20 & 97.00 & 75.50 & **99.70** & **99.70** & 99.64 \\
12 & 89.70 & 91.00 & 94.80 & 89.90 & 90.00 & 90.70 & **99.64** \\
13 & **100.00** & **100.00** & **100.00** & 99.60 & **100.00** & **100.00** & **100.00** \\ OA & 85.09 & 86.18 & 83.31 & 82.58 & 86.50 & 88.48 & **97.13** \\ AA & 77.86 & 79.26 & 77.08 & 74.22 & 79.42 & 81.67 & **99.83** \\ Kappa & 83.39 & 84.60 & 81.51 & 80.57 & 84.96 & 87.16 & **98.74** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **GK-Graph** & **LLR-Graph** & **LLE-Graph** & **NNLRS-Graph** & **SR-Graph** & **PCSSR-Graph** & **CASD Assisted PCSSR** \\ \hline
1 & 37.40 & 33.30 & 24.30 & 41.30 & 36.10 & 73.30 & **100.00** \\
2 & 52.10 & 51.70 & 47.50 & 36.90 & 53.50 & 79.80 & **99.69** \\
3 & 97.20 & 97.10 & 98.40 & 83.90 & 98.10 & 99.10 & **99.11** \\
4 & 82.90 & 84.60 & 90.60 & 98.60 & 84.20 & 95.70 & **100.00** \\
5 & 57.90 & 59.00 & 55.00 & 96.00 & 64.30 & 60.80 & **99.87** \\
6 & 61.70 & 59.20 & 72.40 & 51.50 & 61.80 & 86.00 & **100.00** \\
7 & 5.40 & 6.20 & 3.20 & 40.00 & 6.40 & 10.60 & **99.87** \\
8 & 99.60 & 99.60 & **100.00** & 98.60 & **100.00** & 99.50 & 99.64 \\
9 & 10.20 & 9.40 & 6.80 & 10.60 & 10.00 & 11.40 & **100.00** \\
10 & 21.60 & 24.50 & 33.30 & 39.50 & 25.00 & 37.00 & **99.51** \\
11 & 38.70 & 41.80 & 44.70 & 36.50 & 43.50 & 64.40 & **99.47** \\
12 & 76.90 & 79.10 & 83.20 & 88.60 & 84.10 & 94.30 & **99.47** \\
13 & 95.70 & 95.10 & 98.90 & 90.00 & 97.80 & 98.50 & **99.11** \\
14 & 48.70 & 46.20 & 54.00 & 39.80 & 46.50 & 51.60 & **100.00** \\
15 & 81.70 & 83.00 & 84.20 & 95.20 & 84.70 & 90.00 & **100.00** \\
16 & 94.10 & 95.20 & 94.00 & 39.70 & 95.20 & 93.00 & **99.47** \\ OA & 64.31 & 64.80 & 64.23 & 65.06 & 66.64 & 81.10 & **97.07** \\ AA & 60.11 & 60.31 & 61.91 & 61.67 & 61.95 & 71.56 & **99.70** \\ Kappa & 61.42 & 61.93 & 61.53 & 61.78 & 63.87 & 79.19 & **97.31** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification accuracy of each class, OA, AA and Kappa coefficients for truncated IND PINE data with 16 classes (15 training samples for each class). The highest value of each row is shown in bold. The spatial algorithm performs well only when the samples to predict are close to the labeled samples. As the distance increases, the reliability of prediction will drop. Besides, the classification boundary delineated by the spatial algorithm does not take into account the edge information of the hyperspectral figure. Therefore, samples in the intersecting area between classes are more affected by neighbor samples and more likely to be assigned to an incorrect category. If there are many unlabeled samples near the intersecting area, the classification result based on CASD could be unsatisfactory (Figure 9). To sum up, the classification effect will be relatively poor at the category boundary away from the training samples. Conversely, if the ground blocks to be classified in the dataset are broken and scattered, the classification boundary is more likely to fall in negligible areas (the black background area), and the classification center is more likely to fall within the ground block that needs to be classified. Therefore, with enough training samples given, the classification result on the more scattered dataset are basically better. Figure 8: Classification result of BOT with three training samples per class (OA = 87.1%). (**a**) The classification result of BOT.