# A Novel Spectral-Spatial Classification Method for Hyperspectral Image at Superpixel Level
## 3 Experimental Results
### Classification Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Class** & **Train/Test** & **SVM** & **EPF** & **IFRF** & **SuperPCA** & **PCA-SLIC** & **SSC-SL** \\ \hline Alfalfa & 541 & 49.76 \(\pm\) 16.71 & 512.22 \(\pm\) 40.17 & 94.63 \(\pm\) 6.97 & 95.61 \(\pm\) 1.54 & 100 \(\pm\) 0 & 100.00 \(\pm\) 0.00 \\ Corn-ntill & 143/1285 & 67.87 \(\pm\) 27.3 & 81.5 \(\pm\) 4.22 & 91.27 \(\pm\) 0.67 & 93.6 \(\pm\) 2.34 & 95.86 \(\pm\) 2.4 & 96.91 \(\pm\) 2.53 \\ Corn-mintill & 83/747 & 54.11 \(\pm\) 2.2 & 60.48 \(\pm\) 3.49 & 94 \(\pm\) 2.84 & 95.57 \(\pm\) 3.65 & 95.66 \(\pm\) 3.4 & 96.13 \(\pm\) 4.17 \\ Corn & 24/213 & 38.87 \(\pm\) 7.74 & 66.95 \(\pm\) 25.57 & 87.51 \(\pm\) 5.69 & 87.23 \(\pm\) 6.73 & 93.01 \(\pm\) 7.57 & 93.21 \(\pm\) 6.58 \\ Grass-pasture & 49/434 & 88.48 \(\pm\) 2.99 & 94.45 \(\pm\) 1.25 & 96.08 \(\pm\) 1.73 & 95.83 \(\pm\) 2.45 & 95.85 \(\pm\) 3.19 & 96.17 \(\pm\) 2.83 \\ Grass-trees & 73/657 & 96.41 \(\pm\) 1.37 & 99.85 \(\pm\) 0.16 & 99.09 \(\pm\) 0.72 & 96.74 \(\pm\) 2.76 & 97.42 \(\pm\) 1.87 & 98.85 \(\pm\) 0.35 \\ Grass-pasturemowed & 3/25 & 79.6 \(\pm\) 7.65 & 96.8 \(\pm\) 1.69 & 87.6 \(\pm\) 14.78 & 96.4 \(\pm\) 1.26 & 90.71 \(\pm\) 6.9 & 93.57 \(\pm\) 2.26 \\ Hay-windword & 48/403 & 97.37 \(\pm\) 1.43 & 100 \(\pm\) 0.10 & 99.02 \(\pm\) 1.79 & 99.79 \(\pm\) 0.04 & 100.00 \(\pm\) 0.00 \\ Oats & 2/18 & 26.67 \(\pm\) 9.37 & 22.72 \(\pm\) 7.03 & 15 \(\pm\) 21.12 & 80 \(\pm\) 25.82 & 100 \(\pm\) 0.0 & 100.00 \(\pm\) 0.00 \\ Soybean-ntill & 98/874 & 65.94 \(\pm\) 3.09 & 77.25 \(\pm\) 2.74 & 98.99 \(\pm\) 2.64 & 92.95 \(\pm\) 2.66 & 93.37 \(\pm\) 1 & 93.27 \(\pm\) 2.71 \\ Soybean-mintill & 24/2029 & 84.83 \(\pm\) 0.95 & 96.57 \(\pm\) 0.83 & 98.47 \(\pm\) 0.55 & 98.33 \(\pm\) 1.35 & 97.76 \(\pm\) 1.16 & 98.21 \(\pm\) 1.06 \\ Soybean-clean & 60/533 & 64.43 \(\pm\) 512 & 93.73 \(\pm\) 8.21 & 90.51 \(\pm\) 4.62 & 92.35 \(\pm\) 2.56 & 94.2 \(\pm\) 2.36 & 94.35 \(\pm\) 2.82 \\ Wheat & 21/184 & 97.34 \(\pm\) 2.15 & 99.3 \(\pm\) 0.27 & 99.41 \(\pm\) 0.17 & 97.39 \(\pm\) 1.08 & 98.02 \(\pm\) 1.87 & 98.69 \(\pm\) 0.46 \\ Woods & 127/1138 & 96.57 \(\pm\) 0.64 & 99.48 \(\pm\) 0.33 & 99.68 \(\pm\) 0.31 & 99.13 \(\pm\) 0.88 & 98.62 \(\pm\) 0.98 & 99.49 \(\pm\) 0.38 \\ Building-grass-trees & 39/347 & 49.31 \(\pm\) 7 & 73.31 \(\pm\) 10.92 & 96.89 \(\pm\) 1.94 & 91.96 \(\pm\) 5.5 & 95.9 \(\pm\) 3.01 & 96.61 \(\pm\) 3.68 \\ Stone-steel-towers & 10/83 & 88.07 \(\pm\) 4.66 & 99.28 \(\pm\) 1.9 & 98.68 \(\pm\) 1.33 & 83.73 \(\pm\) 16.25 & 97.69 \(\pm\) 5.19 & 97.74 \(\pm\) 0.79 \\ OA & – & 77.63 \(\pm\) 0.66 & 83.48 \(\pm\) 1.1 & 95.49 \(\pm\) 0.26 & 95.79 \(\pm\) 0.51 & 96.61 \(\pm\) 1.23 & 97.18 \(\pm\) 1.26 \\ AA & – & 71.6 \(\pm\) 4.74 & 80.77 \(\pm\) 6.81 & 89.93 \(\pm\) 4.19 & 93.49 \(\pm\) 4.91 & 96.49 \(\pm\) 2.28 & 97.07 \(\pm\) 2.45 \\ \(\kappa\) & – & 0.7424 \(\pm\) 0.69 & 0.8657 \(\pm\) 1.28 & 0.9485 \(\pm\) 0.3 & 0.9518 \(\pm\) 0.59 & 0.960 \(\pm\) 1.32 & 0.9649 \(\pm\) 1.37 \\ \hline \end{tabular}
\end{table} 
Table 1: The classification results on Indian Pines dataset achieved by SVM, EPF, IFRF, SuperPCA, PCA-SLIC, and the proposal SSB-SL. Figure 5: The classification maps of these six methods on the Indian Pines dataset. (**a**) Reference image. (**b**) SVM. (**c**) EPF. (**d**) IFRF. (**e**) SuperPCA. (**f**) PCA-SLIC. (**g**) SSC-SL. the PCA-SLIC method without using balance parameters. Figure 6 shows the classification maps of these six methods on the Salinas dataset. The University of Pavia dataset has the characteristic of good spatial separation of several classes with similar spectral feature, such as Fallow, Fallowroughplough and Fallowsmooth. There is no doubt that this characteristic not only reduces the possibility of misclassification of the pixels located near class boundary, but also enables the obtained superpixels to have a high purity. Therefore, as shown in Table 3, all five spectral-spatial methods provide good classification results (greater than 95.03%).