# Land Cover Classification Using a KOMPSAT-3A Multi-Spectral Satellite Image
## 3 Results and Discussion  
The main challenge for classification of the KOMPSAT-3A data is that it has only four bands. The classification may not be as good as for other satellite data such as Landsat which provide more than four bands. Hence, to add more variables, the possible ratio and two indices were derived. Figure 4 shows the example of two ratios and two indices examples where we can see some specific objects are clearly separable, even visually. Based on the assumption that derived bands may help in classification, two composite images were created for classification. The first one consisted of only four bands whereas the new derived composite consisted of 12 bands. Figures 5 and 6 show the classified maps for the original and indices and ratio composite case studies for the study area. The results of the classification of the study area were based on the same 70% of the sampled training data set. Similarly, on the basis of the remaining 30% sampled validation data, the accuracy of classification were evaluated. Table 3 shows the PA, UA, OA and Kappa statistics for both composite cases using various classifiers. \begin{table}
\begin{tabular}{c c} \hline \hline
**Class Name** & **Description** \\ \hline Barren & Land areas of exposed soil and barren areas \\ Built-up & Residential, industrial, roads, vinyl houses \\ Farmland & Crop fields and fallow lands \\ Vegetation & Mixed grasslands and forests \\ Water & River and reservoirs \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Land cover classes classified in the study. Figure 4: Example of derived ratio and indices from KOMPSAT-3A bands in the study area: **(a)** Blue/ Green; **(b)** Green/Red; **(c)** Normalized Difference Vegetation Index (NDVI); and **(d)** Normalized Difference Water Index (NDWI). Compositing these bands could provide better classification information for original bands. Figure 5: Land cover classification of the test site using original four bands composite: (**a**) Mahalanobis Distance (MahD); (**b**) Minimum Distance (MinD); (**c**) Maximum Likelihood (ML); and (**d**) Support Vector Machine (SVM). Figure 6: Land cover classification of the test site using ratio and indices bands composite: (**a**) MahD; (**b**) MinD; (**c**) ML; and (**d**) SVM. From Table 3, the well classified land cover seems to be water except in the case of MinD where the UA is very low, i.e., 38.46%. Moreover, it is clear that the overall accuracy of MinD is very poor and is unchanged in the case of the derived composite. MahD showed much improvement of about 10% form 80.93% to 90.68% in overall classification accuracy with the addition of composite ratio and indices, SVM also showed an increase, but only of 1%. Visually, the results of the classification of the study area seem to be very variant in each cases. The classification from MahD and SVM seems to have visually balanced classification of land cover. However, MahD seems to be covered with more vegetation than SVM which is a built-up area. Whereas, MinD and ML shows very poor classification, they were more favored in classification of vegetation and built-up areas, respectively. The vegetation and the farmlands were misclassified by the MahD classifier. Similarly, the ML classifier seems to be confused by barren open areas with built-up, roads and vinyl houses. The misclassifications shown by MinD classifier identified black mulching vinyl in farmland as water class, which was due to its dark nature. Additionally, with its better visual classification, SVM has a salt and pepper effect in areas where vegetation was confused with farmlands and vice versa. SVM is a well-known classifier that produces highly accurate results as expected from a well-trained model. This was also seen in both cases of this study. ML showed higher accuracy compared to MahD and MinD in the first case and the results were more localized in the sampling polygon only. In the second case the results were also localized but with lower accuracy.