# Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation
## 4 Experimental Results
### Remote Sensing Image  
Remote sensing images are more complex than natural images, so some remote sensing images are also selected from different sensors to verify the effectiveness of the proposed algorithm, as shown in Figure 6. Figure 6(a1,b1) shows panchromatic images, Figure 6(c1,d1) shows multispectral images, and Figure 6(e1,f1) shows synthetic aperture radar (SAR) images, and their sensor parameters are listed in Table 4. Figure 6(a2-f2) shows the corresponding ground truth images. Figure 7 is the classification result of remote sensing images, where each column represents scm, FCM_S, FGFCM, RFLICM, GKWFLICM, MSHLICM, VT-HMRF-FCM, and the proposed FVT-FCM. It shows that the four pixel-based algorithms, FCM, FCM_S, FGFCM, and RFLICM, have poor noise resistance. Their performance is very close, except for Figure 7(d2). There are a large number of misclassified pixels in the result. GKWFLICM has a certain degree of noise resistance, but it has poor ability in areas with similar colors, as shown in Figure 7(e3,e5). MSHLICM can overcome most of the noise, and the phenomenon of salt and pepper has significantly improved. But, the classification performance is extremely unstable; the classification results occasionally perform well, as shown in Figure 7(f1,f3), and occasionally perform poorly, as shown in Figure 7(f2,f4,f6). VT-HMRF-FCM, as a region-based classification algorithm, has demonstrated strong noise resistance. However, it is difficult to classify details and boundaries effectively because of the rigidity of polygons, as shown in Figure 7(g3,g4). The proposed algorithm FVT-FCM effectively compensates for VT-HMRF-FCM's shortcomings through a hierarchical fuzzy membership model. It flexibly achieves accurate boundary classification while ensuring noise resistance, as shown in Figure 7(h1-h6). Figure 5: Buffer area analysis of boundaries, where the first row represents UA, the second row represents PA. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **UA (\%)** & **PA (\%)** & **OA (\%)** \\ \hline FCM & 100 & 13.58 & 56.79 \\ FCM\_S & 100 & 68.08 & 84.04 \\ FGFCM & 100 & 21.97 & 60.985 \\ RFLICM & 100 & 26.03 & 63.015 \\ GKWFLICM & 95.10 & 40.05 & 67.575 \\ MSHLICM & 77.95 & 78.35 & 78.15 \\ VT-HMRF-FCM & 93.43 & 89.21 & 91.32 \\ Proposed FVT-FCM & 93.88 & 89.60 & 91.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Boundary accuracy of simulated image classification results. Figure 6: Remote sensing images. (**a1,b1**) Panchromatic images. (**c1,d1**) Multispectral images. (**e1,f1**) SAR images. (**a2–f2**) Corresponding ground truth images. Figure 7: Classification results of remote sensing images. Figure 8 is the contour line images of remote sensing image classification results, where each column represents algorithms FCM, FCM_S, FGFCM, RFLICM, GKWFLICM, MSHLICM, VT-HMRF-FCM, and the proposed FVT-FCM. The content of contour lines can reflect the number of misclassified pixels. It can be seen that FCM, FCM_S, FGFCM, RFLICM, and GKWFLICM have the highest contour line content. This indicates that the five algorithms have poor noise immunity. MSHLICM, VT-HMRF-FCM, and the proposed FVT-FCM are similar, but there are still some redundant contour lines at the boundaries in MSHLICM and VT-HMRF-FCM, which indicates that their performance in boundary classification is not satisfactory. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Images** & **Sensors** & **Spatial Resolution** & **Number of Bands** \\ \hline Image 1 & Cartosat-1 & 2.5 m & 1 \\ Image 2 & WordView-1 & 0.5 & 1 \\ Image 3 & IKONOS & 1 m & 3 \\ Image 4 & Spot-5 & 2.5 m & 3 \\ Image 5 & RADARSAT-I & 30 m & 1 \\ Image 6 & RADARSAT-I & 30 m & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sensor parameters of remote sensing images. Figure 8: Contour line images of remote sensing image classification results.