# A Novel Spectral-Spatial Classification Method for Hyperspectral Image at Superpixel Level
## 3 Experimental Results
### Classification Results
For two superpixel-based methods, SSC-SL and SuperPCA, the proposed method SSC-SL achieves better classification accuracy, which is higher of about 4% than SuperPCA (99.35% vs. 95.03%). Furthermore, the average accuracy of SSC-SL, 99.02%, also indicates that the proposed method can classify each class almost correctly. The characteristic of the class boundsary complexity of this dataset highlights the advantage of the EPF algorithm. This makes the classification results of EPF better than that of other two spectral-spatial methods of IFRF and SuperPCA. Figure 7 shows the classification maps of these six methods on University of Pavia dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **Train/Test** & **SVM** & **EPF** & **IFRF** & **Super-PCA** & **PCA-SLIC** & **SSL-SL** \\ \hline Brookolgreenreved1 & 21/1988 & 97.64 \(\pm\) 1.32 & 99.43 \(\pm\) 0.63 & 100 \(\pm\) 0 & 98.61 \(\pm\) 4.39 & 100 \(\pm\) 0 & 99.95 \(\pm\) 0.01 \\ Brookolgreenreved2 & 38/3688 & 98.81 \(\pm\) 0.19 & 99.86 \(\pm\) 0.08 & 99.34 \(\pm\) 0.47 & 98 \(\pm\) 2.86 & 99.7 \(\pm\) 0 & 99.59 \(\pm\) 0.32 \\ Fallow & 20/1956 & 84.89 \(\pm\) 4.77 & 83.64 \(\pm\) 5.08 & 99.95 \(\pm\) 0.15 & 98.99 \(\pm\) 0.2 & 99.99 \(\pm\) 0.7 & 99.86 \(\pm\) 0.13 \\ Fallowowplow & 14/1380 & 98.96 \(\pm\) 0.4 & 99.37 \(\pm\) 0.34 & 96.63 \(\pm\) 1.84 & 94.51 \(\pm\) 4.95 & 89.45 \(\pm\) 9.3 & 90.17 \(\pm\) 8.6 \\ Fallowsmooth & 27/2651 & 97.26 \(\pm\) 1.15 & 99.55 \(\pm\) 0.28 & 99.27 \(\pm\) 0.27 & 95.95 \(\pm\) 5.8 & 97.26 \(\pm\) 1.08 & 98.74 \(\pm\) 0.26 \\ Stubble & 40/3919 & 99.56 \(\pm\) 0.11 & 99.97 \(\pm\) 0.02 & 99.92 \(\pm\) 0.03 & 96 \(\pm\) 5.1 & 99.92 \(\pm\) 0.01 & 99.99 \(\pm\) 0.01 \\ Celery & 36/3543 & 99.33 \(\pm\) 0.25 & 99.74 \(\pm\) 0.02 & 99.66 \(\pm\) 0.2 & 96.46 \(\pm\) 2.27 & 99.22 \(\pm\) 0.02 & 99.92 \(\pm\) 0.02 \\ Grapesuntrained & 13/11185 & 88.53 \(\pm\) 2.05 & 91.35 \(\pm\) 1.9 & 92.83 \(\pm\) 0.81 & 97.74 \(\pm\) 1.59 & 97.83 \(\pm\) 1.18 & 99.1 \(\pm\) 0.23 \\ Solivnyarddevelop & 63/6140 & 98.54 \(\pm\) 0.8 & 99.5 \(\pm\) 0.32 & 99.98 \(\pm\) 0 & 97.91 \(\pm\) 3.54 & 99.89 \(\pm\) 0.18 & 99.68 \(\pm\) 0.29 \\ Cornresenecodgreen & 33/3245 & 87.34 \(\pm\) 4.17 & 92.07 \(\pm\) 3.35 & 99.67 \(\pm\) 0.22 & 95.88 \(\pm\) 2.2 & 95.33 \(\pm\) 3.9 & 97.51 \(\pm\) 0.62 \\ Lettrecuramine & & & & & & & \\
4 wk & 11/1057 & 90.67 \(\pm\) 1.85 & 97.02 \(\pm\) 1.38 & 90.59 \(\pm\) 4.39 & 73.8 \(\pm\) 22.74 & 94.4 \(\pm\) 3.98 & 96.69 \(\pm\) 4.25 \\ Lettrecuromaine & & & & & & & \\
5 wk & 20/1907 & 99.76 \(\pm\) 0.31 & 100 \(\pm\) 0 & 100 \(\pm\) 0.02 & 89.24 \(\pm\) 8.71 & 95.11 \(\pm\) 0.91 & 98.58 \(\pm\) 1.06 \\ Lettrecuramine & & & & & & & \\
7 wk & 11/1059 & 90.93 \(\pm\) 4.02 & 94.24 \(\pm\) 0.86 & 92.6 \(\pm\) 1.32 & 83.9 \(\pm\) 10.31 & 88.59 \(\pm\) 6.91 & 95.75 \(\pm\) 0.11 \\ Vinyardunteanized & 73/7195 & 55.39 \(\pm\) 4.96 & 62.68 \(\pm\) 5.37 & 93.97 \(\pm\) 1.84 & 96.66 \(\pm\) 3.92 & 94.98 \(\pm\) 2.41 & 95.55 \(\pm\) 1.8 \\ Vinyardvertical & 19/1788 & 95.35 \(\pm\) 3.74 & 97.71 \(\pm\) 1.87 & 98.98 \(\pm\) 0.86 & 92.25 \(\pm\) 4.77 & 98.66 \(\pm\) 2.4 & 99.78 \(\pm\) 0.01 \\ OA & – & 89.16 \(\pm\) 0.68 & 91.68 \(\pm\) 0.75 & 96.8 \(\pm\) 0.27 & 95.90 \(\pm\) 0.62 & 97.39 \(\pm\) 0.56 & 98.41 \(\pm\) 0.13 \\ AA & – & 92.54 \(\pm\) 1.92 & 94.62 \(\pm\) 1.36 & 96.55 \(\pm\) 0.96 & 93.66 \(\pm\) 5.8 & 96.27 \(\pm\) 2.49 & 98.01 \(\pm\) 1.1 \\ \(\kappa\) & – & 0.879 \(\pm\) 0.76 & 0.907 \(\pm\) 0.85 & 0.9644 \(\pm\) 0.3 & 0.9543 \(\pm\) 0.69 & 0.9710 \(\pm\) 0.63 & 0.9823 \(\pm\) 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The classification results on the Salinas dataset provided by SVM, EPF, IFRF, SuperPCA, PCA-SLIC, and the proposal SSB-SL. Figure 6: The classification maps of these six methods on Salinas dataset. (**a**) Reference image. (**b**) SVM. (**c**) EPF. (**d**) IFRF. (**e**) SuperPCA. (**f**) PCA-SLIC. (**g**) SSC-SL.