# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 3 Results and Discussion
### Experimental Result and Analysis
#### 3.4.4 Results from the Houston 2018 Dataset  
The quantitative results for the Houston 2018 dataset with 0.2% training samples from each class are presented in Table 5. The corresponding classification map is shown in Figure 14. The best results from the tables are displayed in bold font for comparison. As observed from Table 5, the proposed methods are robust and achieve good classification results even for challenging scenes. The proposed approach improves accuracy from 68.19% to 83.57% for the SVM method. In this case also, the superpixel-based approaches (SCMK, R2 MK) display superior performance as compared to non-superpixel based methods (EPF, 2 DSSA). Here also, multi-scale window approaches (ASMGSK, MsuperPCA, and 2D-MSSA) outperform fixed-window based methods as different scales of spatial features are incorporated into the analysis. Figure 14 also highlights the superiority of the proposed method. The salt and pepper noise is reduced by a greater extent, and a smoother classification map is produced with the proposed method.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Class** & **Samples** & **SVM** & **EPF** & **SCMK** & **R2 MK** & **ASMGSK** & **MsuperPCA** & **2 DSSA** & **2 DMSSA** & **SP-SSA** \\ \hline Healthy grass & 9799 & 62.84 & 65.71 & 73 & 74.49 & 81.46 & 76.44 & 76.64 & **84.51** & 79.28 \\ Stressed grass & 32,502 & 84.4 & 85.08 & 83.83 & 86.42 & 89.88 & 88.67 & 83.7 & 90.99 & **91.33** \\ Artificial turf & 684 & 100 & 98.83 & 100 & 99.41 & **100** & **100** & **100** & **100** & **100** \\ Evergreen trees & 13,588 & 83.72 & 73.99 & 80.15 & 84.76 & 87.77 & 82.43 & 80.43 & **91.13** & 87.37 \\ Deciduous trees & 5048 & 43.98 & 36.75 & 50.54 & 51.74 & 63.82 & 55.89 & 46.38 & 58.26 & **73.94** \\ Bare earth & 4516 & 79.32 & 82.67 & 86.28 & 88.46 & 94.01 & 91.1 & 80.64 & 89.04 & **96.21** \\ Water & 266 & 66.42 & 67.8 & 61.89 & 68.06 & 83.08 & 68.18 & 74.62 & 66.92 & **85.71** \\ Residential buildings & 39,762 & 68.26 & 77.29 & 77.52 & 79.2 & 84.37 & 82 & 74.76 & 78.07 & **87.39** \\ Non-residential buildings & 223,684 & 82.57 & 85.51 & 87.3 & 88.31 & 91.64 & 89.18 & 86.05 & 90.63 & **92.73** \\ Roads & 45,810 & 40.41 & 43.7 & 46.79 & 47.33 & 58.86 & 51.39 & 42.75 & 53.25 & **63.89** \\ Sidewalks & 34,002 & 31.36 & 35.3 & 37.58 & 37.51 & 43.74 & 39.25 & 33.65 & **51.35** & 49.71 \\ Crosswalks & 1516 & 4.96 & 9.02 & 8.63 & 9.12 & 13.66 & 9.26 & 7.62 & 5.2 & **14.21** \\ Major thoroughfares & 46,358 & 50.97 & 55.91 & 59.41 & 63.04 & 72.53 & 65.1 & 58.17 & 60.83 & **75.48** \\ Highways & 9849 & 49.7 & 60.35 & 61.19 & 63.93 & 73.83 & 70.78 & 59.55 & 68.92 & **78.56** \\ Railways & 6937 & 79.92 & 85.83 & 92 & 89.52 & 95.29 & 88.07 & 80.22 & 95.84 & **97.29** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification results for the Houston Dataset with 0.2% training for SVM, EPF, SCMK, R2 MK, ASMGSSK, MsuperPCA, 2D-SSA, 2D-MSSA, and SP-SSA algorithms.  
Figure 13: (**a**) Ground Truth Image, Classification Maps of (**b**) SVM (**c**) EPF (**d**) SCMK (**e**) R2 MK (**f**) ASMGSSK (**g**) MsuperPCA (**h**) 2D-SSA (**i**) 2D-MSSA (**j**) SP-SSA for Salinas dataset.