# Spatial Perception Correntropy Matrix for Hyperspectral Image Classification
## 4 Experimental Results
### Performance Evaluation
#### 4.3.3 Experiments with the Botswana Data Set  
The last experiment was carried out on the Botswana data set, setting the number of training samples per class to 1% of the total samples, while the others were used for testing. This data set is collected by satellite, with low spatial resolution, complex shape, variety and irregular distribution of ground objects, so it poses a great challenge to many methods. On this data set, the hyperparameters were set to \(L=9,S=35,K=45\). Table 3 lists the accuracy of different methods. As we can observe, RPNet obtains the worst accuracy. The reason for this may be that it is difficult to learn effective features for HSI with many details when the training sample is small. Compared with other methods, the proposed SPCM has significant improvement with respect to OA, AA, and Kappa. This also demonstrates the advantage of the joint learning of spectral-spatial correlation. Finally, the full classification maps of different methods are also shown in Figure 9. We can find that the result achieved by the proposed method is the best, as the details of HSI are well preserved.  
In summary, as shown by the experimental results, the SPCM method proposed in this paper performs better than other state-of-the-art methods on three typical data sets. This section demonstrates that the spectral and spatial correlation is very important for HSI classification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **LCMR** & **SCMK** & **CEGCN** & **RPNet** & **LCEM** & **SPCM** \\ \hline
1 & 95.02 & 86.39 & **100.00** & 61.80 & 94.01 & 99.79 \\
2 & 84.55 & 55.29 & 76.28 & 21.00 & 87.47 & **100.00** \\
3 & 28.60 & 88.55 & **100.00** & 61.69 & 73.15 & 79.26 \\
4 & 96.29 & 77.55 & 75.23 & 29.58 & 74.25 & **98.17** \\
5 & 72.66 & 83.38 & 29.05 & **84.59** & 76.62 & 82.79 \\
6 & 71.57 & 67.78 & 56.65 & 62.03 & 57.37 & 69.30 \\
7 & 84.90 & 89.73 & **100.00** & **100.00** & 77.30 & 88.80 \\
8 & 61.99 & **84.85** & 71.50 & 70.65 & 77.25 & 80.17 \\
9 & **95.95** & 89.84 & 94.38 & 87.46 & 88.35 & 79.18 \\
10 & **99.59** & 85.88 & 56.55 & 99.19 & 75.27 & 78.96 \\
11 & 88.31 & 86.88 & **100.00** & 72.19 & 86.01 & 87.45 \\
12 & 32.89 & 87.21 & 85.87 & **93.30** & 53.69 & 59.96 \\
13 & 85.49 & 78.04 & 89.27 & 83.77 & 89.13 & **92.33** \\
14 & 31.49 & 75.11 & **89.24** & 12.77 & 44.47 & 60.52 \\ \hline OA & 76.62 & 80.56 & 80.47 & 72.34 & 77.20 & **83.53** \\ AA & 73.52 & 81.18 & 80.29 & 67.14 & 75.31 & **82.68** \\ Kappa & 74.63 & 79.45 & 78.82 & 69.95 & 75.24 & **82.13** \\ \hline \end{tabular}
\end{table}
Table 3: Classification results obtained by LCMR, SCMK, CEGCN, RPNet, LCEM, and SPCM using 1% of the available labelled data for training with the Botswana data set. The best results are expressed in bold.  
Figure 8: Classification maps of different methods on the KSC data set. (**a**) False Colour Image; (**b**) Ground Truth; (**c**) Training set (**d**) Test set; (**e**) LCMR; (**f**) SCMK; (**g**) CEGCN; (**h**) PRNet; (i) LCEM; (j) SPCM.  
Figure 9: Classification maps of different methods on the Botswana data set. (**a**) False Colour Image; (**b**) Ground Truth; (**c**) Training set; (**d**) Test set; (**e**) LCMR; (**f**) SCMK; (**g**) CEGCN; (**h**) PRNet; (**i**) LCEM; (**j**) SPCM.