## 4 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Class** & \(Accu_{y}\) & \(Prec_{n}\) & \(Reca_{I}\) & \(F_{score}\) & **AUC Score** & **MCC** \\ \hline
**Training Phase** & & & & & & \\ Grassland & 97.30 & 95.29 & 95.67 & 95.48 & 96.83 & 93.55 \\ Agriculture & 97.88 & 94.16 & 92.81 & 93.48 & 95.84 & 92.22 \\ Barren land & 97.06 & 92.19 & 94.65 & 93.40 & 96.20 & 91.53 \\ Water & 97.65 & 97.00 & 95.57 & 96.28 & 97.10 & 94.57 \\ \hline
**Average** & **97.47** & **94.66** & **94.67** & **94.66** & **96.49** & **92.97** \\ \hline
**Testing Phase** & & & & & & \\ Grassland & 98.12 & 95.65 & 98.51 & 97.06 & 98.23 & 95.70 \\ Agriculture & 98.59 & 95.56 & 97.73 & 96.63 & 98.27 & 95.75 \\ Barren land & 98.59 & 97.73 & 95.56 & 96.63 & 97.48 & 95.75 \\ Water & 98.12 & 98.18 & 94.74 & 96.43 & 97.05 & 95.18 \\ \hline
**Average** & **98.36** & **96.78** & **96.63** & **96.69** & **97.76** & **95.60** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification outcome of TSADL-LULCCD approach on 80:20 of TR/TS databases. Figure 5: Average outcome of TSADL-LULCCD method on 80:20 of TR/TS databases. The TLS and VLS of the TSADL-LULCCD approach are tested on 80:20 of TR/TS databases in Figure 7. The figure exhibited shows that the TSADL-LULCCD method has revealed better performance with the least values of TLS and VLS. Seemingly, the TSADL-LULCCD algorithm has resulted in reduced VLS outcomes. Figure 6: TACC and VACC analysis of TSADL-LULCCD method on 80:20 of TR/TS databases. Figure 7: TLS and VLS analysis of TSADL-LULCCD approach on 80:20 of TR/TS databases. A clear precision-recall investigation of the TSADL-LULCCD approach on 80:20 TR/TS databases is seen in Figure 8. The results show the TSADL-LULCCD method has enhanced values of precision-recall values. The detailed ROC study of the TSADL-LULCCD technique on 80:20 TR/TS databases is shown in Figure 9. The outcomes denoted the TSADL-LULCCD algorithm has shown its ability to categorize different classes under a test database. In Table 3 and Figure 10, the overall classification outcomes of the TSADL-LULCCD method with 70:30 of the TR/TS database are provided. On the TR database, the TSADL  
Figure 8: Precision-recall analysis of TSADL-LULCCD approach on 80:20 of TR/TS databases. Figure 9: ROC analysis of TSADL-LULCCD approach on 80:20 of TR/TS databases. LULCCD approach has recognized grassland samples with \(accu_{y}\) of 96.37%, \(prec_{n}\) of 91.77%, \(rec_{l}\) of 96.36%, \(F_{score}\) of 94.01%, \(AUC_{score}\) of 96.37%, and MCC of 91.47%. In the meantime, the TSADL-LULCCD method has identified agriculture samples with \(accu_{y}\) of 96.64%, \(prec_{n}\) of 92.74%, \(rec_{l}\) of 87.79%, \(F_{score}\) of 90.20%, \(AUC_{score}\) of 93.16%, and MCC of 88.22%. Additionally, the TSADL-LULCCD model has categorized barren land samples with \(accu_{y}\) of 98.12%, \(prec_{n}\) of 96.67%, \(rec_{l}\) of 94.16%, \(F_{score}\) of 95.39%, \(AUC_{score}\) of 96.65%, and MCC of 94.23%. On the TR database, the TSADL-LULCCD method has recognized grassland samples with \(accu_{y}\) of 98.44%, \(prec_{n}\) of 96.15%, \(rec_{l}\) of 99.01%, \(F_{score}\) of 97.56%, \(AUC_{score}\) of 98.59%, and MCC of 96.43%. In the meantime, the TSADL-LULCCD approach has identified agriculture samples with \(accu_{y}\) of 99.38%, \(prec_{n}\) of 100.00%, \(rec_{l}\) of 96.15%, \(F_{score}\) of 98.04%,