# Land Cover Classification Using a KOMPSAT-3A Multi-Spectral Satellite Image
## 4 Conclusions  
Advancements in science and technology have improved the satellite sensors and new sets of sensors are being added annually with improved quality. These improvements in spatial, radiometric or temporal improvement have been found to be useful in various applications for the management of earth resources. KOMPSAT-3A is the newest family of KOMPSAT satellite constellation launched by KARI of Korea, which provides four high resolution RGB-NIR multispectral data sets along with very high resolution panchromatic images. In this study, different classification techniques have been examined for the classification of land cover in a rural area of southwestern Korea. Various supervised classifiers were applied to examine the classification of KOMPSAT-3A data in two cases: original composite and derived ratio and indices composite. Due to minimum bands and high resolution, the classification was not as efficient as \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{**Water**} & \multicolumn{2}{c}{**Vegetation**} & \multicolumn{2}{c}{**Farmland**} & \multicolumn{2}{c}{**Barren**} & \multicolumn{2}{c}{**Built-up**} & \multirow{2}{*}{**OA**} & \multirow{2}{*}{**Kappa**} \\  & & **PA** & **UA** & & & & & & & & & & & \\ \hline \multirow{4}{*}{Original} & MahD & 100 & 90.85 & 96.48 & 52.39 & 78.47 & 92.96 & 84.56 & 85.60 & 51.84 & 98.49 & 80.93 & 0.682 \\  & MinD & 100 & 38.46 & 91.09 & 48.74 & 65.84 & 90.87 & 47.69 & 53.77 & 24.51 & 60.83 & 67.33 & 0.493 \\  & ML & 99.33 & 100 & 97.36 & 68.57 & 87.11 & 99.00 & 98.49 & 71.80 & 69.77 & 85.76 & 88.90 & 0.812 \\  & SVM & 99.69 & 100 & 76.62 & 83.18 & 95.94 & 93.52 & 96.55 & 90.25 & 86.20 & 97.66 & 92.46 & 0.862 \\ \hline \multirow{4}{*}{Ratio \& index} & MahD & 99.69 & 100 & 96.32 & 70.84 & 90.34 & 97.07 & 97.37 & 82.86 & 68.98 & 99.89 & 90.68 & 0.838 \\  & MinD & 100 & 38.46 & 91.09 & 48.74 & 65.84 & 90.87 & 47.69 & 53.77 & 24.51 & 60.83 & 67.33 & 0.493 \\ \cline{1-1}  & ML & 99.59 & 100 & 98.65 & 58.90 & 81.33 & 99.44 & 99.14 & 92.07 & 92.34 & 85.27 & 87.20 & 0.791 \\ \cline{1-1}  & SVM & 99.74 & 100 & 83.36 & 85.98 & 96.47 & 95.24 & 97.41 & 91.42 & 87.11 & 97.25 & 93.98 & 0.890 \\ \hline \hline \end{tabular} PA, UA and OA are Producer’s Accuracy, User’s Accuracy and Overall Accuracy. All are represented in percentage (%).  
\end{table} 
Table 3: Accuracy measures of Mahalanobis Distance (MahD), Minimum Distance (MinD), Maximum Likelihood (ML) and Support Vector Machine (SVM) classifiers in the study area. expected for large areas. MahD and SVM showed better classification accuracy and also improved with the addition of derived bands, whereas the MinD showed lower overall accuracy and was unchanged. ML misinterpreted the classification of barren and urban areas and accuracy dropped with the derived ratio and indices composite. The classification does not seem to be uniform in separating farmland and vegetation, barren and built-up areas. The main reason behind that is the seasonal variability in the applied case. In conclusion, the high resolution RGB-NIR bands are not very efficient at pixel-based automatic classification of land covers. Large areas require larger training sets and seasonal variability often affects the result very much in such datasets. The natural color composite or panchromatic are good sources for monitoring and examination of land cover and are best used as the verification or update data in mapping from mid-resolution multispectral data. Also, deriving ratio and indices seems to improve the accuracy in some classifiers, which is good for the KOMPSAT-3A dataset. In the future, different areas with different seasons and complex lands cover need to be explored to better examine the classification usability of the dataset provided by KOMSAT-3A. This study was supported by 2016 Research Grant from Kangwon National University (No. 520160528). The work presented in this paper is a collaborative development by all authors.