# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 5 Results and Discussion
### Comparison with the State-of-the-Art
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Sequence 1**} & \multirow{2}{*}{**Class**} & \multirow{2}{*}{**\# of Pixels**} & \multicolumn{3}{c}{**PMO**} & \multicolumn{3}{c}{**PILR**} & \multicolumn{3}{c}{**BP**} \\ \cline{3-10}  & & & \(f_{1}\)**(\%)** & \(AA\)**(\%)** & \(OA\)**(\%)** & \(f_{1}\)**(\%)** & \(AA\)**(\%)** & \(OA\)**(\%)** & \(f_{1}\)**(\%)** & \(AA\)**(\%)** & \(OA\)**(\%)** \\ \hline \multirow{8}{*}{Sequence 1} & Soybean & 242 K & 79.81 & 76.96 & 40.47 & 37.71 & & 79.76 & 76.95 & \multirow{8}{*}{84.26} \\  & Corn & 891 & 77.91 & 87.65 & 24.63 & 20.76 & & 77.91 & 87.65 & \\  & Cotton & 12 K & 78.49 & 96.13 & & 28.97 & 40.63 & & 78.22 & 96.13 & \\  & Sorghum & 242 & 59.97 & 100 & & 8.46 & 17.35 & & 59.99 & 100 & \\  & NCC & 892 & 61.12 & 99.77 & & 5.26 & 15.69 & & 61.11 & 99.77 & \\  & Pasture & 46 K & **85.04** & 89.85 & & **28.27** & 46.34 & & **84.98** & 89.85 & \\  & Eucalyptus & 17 K & 92.85 & 92.95 & & 48.57 & 52.49 & & 92.85 & 92.95 & \\  & Soil & 340 K & 87.34 & 87.47 & & 49.69 & 42.51 & & 87.28 & 87.37 & \\  & Grass & 112 & 29.71 & 100 & & 4.84 & 99.11 & & 29.71 & 100 & \\  & Cerrado & 17 K & 84.43 & 91.49 & & 12.88 & 31.63 & & 84.43 & 91.49 & \\ \hline Average & - & - & 73.67 & 92.23 & 84.31 & 25.2 & 40.42 & 40.94 & 73.63 & 92.21 & 84.26 \\ \hline \multirow{8}{*}{Sequence 2} & Corn & 84 K & 62.33 & 78.55 & & 29.88 & 43.94 & & 61.96 & 80.22 & \\  & Cotton & 257 K & 87.28 & 95.94 & & 55.19 & 47.86 & & 87.43 & 94.96 & \\  & Sorghum & 6.3 K & **81.17** & 79.83 & & **1.88** & 1.79 & & **76.56** & 79.83 & \\  & NCC & 18 K & 76.96 & 83.62 & & 27.45 & 42.24 & & 78.63 & 87.63 & \\  & Pasture & 58 K & 90.01 & 90.51 & 77.98 & 36.17 & 35.49 & & 89.15 & 89.79 & \\  & Eucalyptus & 17 K & 90.67 & 97.67 & & 32.79 & 63.79 & & 90.49 & 97.77 & 77.32 \\  & Soil & 220 K & 65.99 & 50.47 & & 39.07 & 32.36 & & 65.54 & 50.01 & \\  & Grass & 112 & 66.46 & 100 & & 13.56 & 99.11 & & 65.68 & 100 & \\  & Cerrado & 17 K & 84.99 & 90.35 & & 21.17 & 30.31 & & 85.26 & 90.46 & \\ \hline Average & - & - & 78.11 & 85.21 & 77.98 & 28.57 & 44.09 & 40.63 & 77.86 & 85.63 & 77.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The overall performance reached by the proposed methodology with post-processing techniques. The most significant differences between the values of the techniques are highlighted. PMO: post-processing technique based on morphological operations. PILR: post-processing based on iterative label refinement; BP: before post-processing. From Table 6, it can be seen that the proposed methodology with post-processing reaches the highest scores in almost all metrics for image sequences 1 and 2. The closest competitor from the state-of-the-art is represented by the FCNN approach reported in [34] with an \(OA\approx 3.31\%\) higher for image sequence 1 and \(\approx 2.98\%\) lower for image sequence 2. However, the nice property of the proposed methodology is that, when it did not win, its \(OA\) achieved is very close to the highest one. The major advantage of the proposed methodology over state-of-the-art approaches is the improvement in terms of classification accuracy. This slight improvement is attributed