# Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information
## 4 Experiments and Analyses
### Comparison Experiments
#### 4.2.1 Experiment Using Indian Pines Dataset  
First, the comparison experiments were performed on the Indian Pines dataset. The obtained experimental results are shown in Figure 8 and Table 3. In the experiment, the obtained results were averaged by ten times.  
Figure 8a shows the results of parameter UE using 8 algorithms. The smaller value of the UE indicated the smaller error and the better the segmentation performance. From the figure, the UE value using CV-CHD and IM-CHD is obviously lower than that using the other six algorithms. In details, from Table 3, compared with FCC-CHD, the error rate of IM-CHD(UE) was reduced by more than 15% compared with CV-CHD, the error rate of IM-CHD(UE) was reduced by more than 8%.  
Figure 8b shows the results of the parameter BR using eight algorithms. The larger value of BR indicated that the boundary obtained by superpixel segmentation was closer to the natural boundary. From the results, CV-CHD and IM-CHD were superior to FPC-CHD and FCC-CHD. In addition, all the above four algorithms (CV-CHD, IM-CHD, FPC-CHD, and FCC-CHD) were much better than the other algorithms. From Table 3, compared with FCC-CHD, the missed boundaries in the IM-CHD algorithm were reduced by more than 5%. Compared with CV-CHD, the missed boundaries were reduced by 8%.  
Figure 8c shows the results of the parameter ASA using eight algorithms. The larger ASA value indicated higher accuracy of the segmentation result. From the figure, the obtained ASA values using different methods were relatively stable. Based on ASA, CV-CHD, IM-CHD, FCC-CHD, and FPC-CHD had relatively better performance. The segmentation accuracy of CV-CHD was similar to that of FCC-CHD. The ASA value of IM-CHD was slightly lower. But compared with the maximum ASA value obtained by the FCC-CHD method, the ASA value of IM-CHD was reduced by less than 2%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(m\) & **5** & **9** & **11** & **14** & **17** & **20** \\ \hline
**UE** & 0.0149 & 0.0156 & 0.0203 & 0.0203 & 0.0179 & 0.0203 \\
**BR** & 0.6511 & 0.6470 & 0.6580 & 0.6580 & 0.6580 & 0.6610 \\
**ASA** & 0.8491 & 0.8661 & 0.8408 & 0.8655 & 0.8655 & 0.8650 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Superpixel segmentation performance under different values of \(m\) (\(\sigma=0.05\)) on Indian Pines.  
Figure 8: The superpixel segmentation results of different methods on Indian Pines: (**a**) UE; (**b**) BR; (**c**) ASA.  
By comprehensively considering the values of the three evaluation indicators, it can be concluded that the segmentation results obtained by the IM-CHD method are the best.  
Figure 9 shows the segmentation results of the Indian Pines dataset under different segmentation methods. Under the same number of iterations, the number of superpixels obtained by different algorithms was different. More obtained superpixels indicated that the segment algorithm was more efficient. From the results, the IM-CHD method can better capture the boundary information and can allocate more superpixels to represent complex areas.  
In the three methods, i.e., FPC-SLIC, FPC-LSC, and FPC-CHD, the images were segmented according to the first principal component map of the HSIS. Therefore, the obtained colors using these three methods were slightly different from the other five methods.  
From Figure 9, the superpixel results using different segmentation methods were slightly different. The CV-CHD and IM-CHD methods can better segment the real boundaries of ground objects, such as areas within a red circle. In both algorithms, more superpixel blocks were assigned to represent the regions with complex colors while fewer superpixels were used for the regions with simple colors. Therefore, more precise results can be obtained using both CV-CHD and IM-CHD.