# A Novel Spectral-Spatial Classification Method for Hyperspectral Image at Superpixel Level
## 3 Experimental Results
### Effect of Different Numbers of Training Samples  
The parameters used in EPF, IFRF, and SuperPCA are kept the same as original references. As shown in Figure 8, the classification accuracies of these six methods present an upward trend with the increase of label proportion on the three datasets. The proposed SSC-SL approach outperforms the other five compared methods for different label ratios of training samples on these three datasets. With the increase of label ratio, the strategy of randomly labeling pixels may not result in a significant increase of the number of labeled superpixels since it is possible to label more samples within a marked superpixel. In spite of this, the classifier we design still shows better classification performance. In particular, our classification result is about 3% better than that of EPF and IFRF on University of Pavia image in the case of marking limited samples (2%). The fact that SSC-SL is superior to PCA-SLIC on different datasets and for different label ratios, confirms the success and effectiveness of the improved SLIC algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Class** & **Train/Test** & **SVM** & **EPF** & **IFRF** & **Super-PCA** & **PCA-SLIC** & **SSB-SL** \\ \hline Broccoligereweed1 & 664/5967 & 91.68 \(\pm\) 0.55 & 99.67 \(\pm\) 0.21 & 98.42 \(\pm\) 0.21 & 95.55 \(\pm\) 0.46 & 99.6 \(\pm\) 0.23 & 99.62 \(\pm\) 0.2 \\ Broccoligereweed2 & 1865/16784 & 97.4 \(\pm\) 0.3 & 99.94 \(\pm\) 0.02 & 99.93 \(\pm\) 0.05 & 99.3 \(\pm\) 0.07 & 99.64 \(\pm\) 0.22 & 99.77 \(\pm\) 0.13 \\ Fallow & 210/1889 & 70.05 \(\pm\) 2.22 & 72.32 \(\pm\) 5.26 & 91.08 \(\pm\) 4.18 & 95.74 \(\pm\) 0.85 & 99.02 \(\pm\) 0.53 & 97.98 \(\pm\) 0.58 \\ Fallowowonghoughough & 307/2757 & 93.82 \(\pm\) 0.95 & 98.23 \(\pm\) 0.66 & 94.85 \(\pm\) 0.86 & 83.71 \(\pm\) 1.63 & 97.29 \(\pm\) 0.75 & 97.36 \(\pm\) 0.58 \\ Fallowomooth & 135/1210 & 99.45 \(\pm\) 0.18 & 99.93 \(\pm\) 0.03 & 99.63 \(\pm\) 0.18 & 91.23 \(\pm\) 1.25 & 96.68 \(\pm\) 2.17 & 99.78 \(\pm\) 0.04 \\ Stubble & 503/4526 & 76.69 \(\pm\) 1.25 & 94.21 \(\pm\) 1.71 & 99.8 \(\pm\) 0.07 & 98.21 \(\pm\) 0.37 & 99.97 \(\pm\) 0.04 & 99.8 \(\pm\) 0.12 \\ Celery & 133/1197 & 82.87 \(\pm\) 0.96 & 94.05 \(\pm\) 1.2 & 98.52 \(\pm\) 0.56 & 98.47 \(\pm\) 0.42 & 99.92 \(\pm\) 0 & 98.55 \(\pm\) 0.93 \\ Grapesuntrained & 369/3313 & 88.37 \(\pm\) 1.32 & 98.86 \(\pm\) 0.3 & 89.36 \(\pm\) 1.74 & 97.21 \(\pm\) 0.36 & 99.23 \(\pm\) 0.35 & 98.66 \(\pm\) 0.38 \\ Solvinneyarddevelop & 95/852 & 98.88 \(\pm\) 0.38 & 98.09 \(\pm\) 0.63 & 57.56 \(\pm\) 4.6 & 95.82 \(\pm\) 0.79 & 99.25 \(\pm\) 0.71 & 99.65 \(\pm\) 0.11 \\ OA & – & 91.35 \(\pm\) 0.18 & 97.43 \(\pm\) 0.24 & 96.98 \(\pm\) 0.19 & 95.03 \(\pm\) 0.69 & 99.31 \(\pm\) 0.09 & 99.35 \(\pm\) 0.09 \\ AA & – & 88.8 \(\pm\) 0.9 & 95.03 \(\pm\) 1.11 & 92.13 \(\pm\) 1.38 & 96.77 \(\pm\) 0.17 & 98.96 \(\pm\) 0.56 & 99.02 \(\pm\) 0.34 \\ \(\kappa\) & – & 0.8844 \(\pm\) 0.23 & 0.9658 \(\pm\) 0.32 & 0.96 \(\pm\) 0.26 & 0.957 \(\pm\) 0.23 & 0.9913 \(\pm\) 0.12 & 0.9915 \(\pm\) 0.12 \\ \hline \end{tabular}
\end{table}
Table 3: The classification results on the University of Pavia dataset obtained by SVM, EPF, IFRF, SuperPCA, PCA-SLIC, and the proposal SSB-SL.  
Figure 7: The classification maps of these six methods on the University of Pavia dataset. (**a**) Reference image. (**b**) SVM. (**c**) EPF. (**d**) IFRF. (**e**) SuperPCA. (**f**) PCA-SLIC. (**g**) SSC-SL.