# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 5 Results and Discussion
### Comparison between the Results of Post-Processing Techniques  
To explore the advantage of adding the post-processing phase, this work evaluates two post-processing techniques named: post-processing based on morphological operations (PMO) and post-processing based on iterative label refinement (PILR). By taking average of all \(f_{1}\), \(AA\), and \(OA\) metrics in Table 5.  
It can be seen that PMO reaches the highest accuracy scores, and just 0.04%, 0.02%, and 0.05% higher than \(f_{1}\), \(AA\), and \(OA\), respectively achieved by the proposed methodology before post-processing (proposed methodology - BP) for sequence 1. However, for sequence 2, PMO is just 0.25%, \(-0.3\)%, and 0.66% higher than \(f_{1}\), \(AA\), and \(OA\), respectively. On one hand, obtained results demonstrate that the PMO technique slightly improves the overall results achieved by PILR technique. This behavior may be attributed to the distribution of data in the database since the PILR technique removes some minority classes and misclassified them with the class containing the majority samples. On the other hand, the filter size used in PMO is suitable for the images analyzed because it is small, and consequently, significant information is not removed by the technique. Thus, although PILR has been demonstrated to be stronger in other works [56], the PMO technique is established in the proposed methodology as a suitable post-processing technique for crop classification. Results for the proposed methodology-BP are also reported in Table 5. \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \multirow{2}{*}{**Sequence**} & \multirow{2}{*}{**Class**} & \multirow{2}{*}{**\# of Pixels**} & \multicolumn{3}{c}{**2D-CNN 1**} & \multicolumn{3}{c}{**2D-CNN 2**} & \multicolumn{3}{c}{**RNN**} \\ \cline{3-11}  & & & \(f_{1}\)**(\%)** & \(AA\)**(\%)** & \(OA\)**(\%)** & \(f_{1}\)**(\%)** & \(AA\)**(\%)** & \(OA\)**(\%)** & \(f_{1}\)**(\%)** & \(AA\)**(\%)** & \(OA\)**(\%)** \\ \hline \multirow{9}{*}{Sequence 1} & Soybean & 242 K & 79.76 & 76.95 & & 80.03 & 82.72 & & 70.67 & 66.37 & \multirow{9}{*}{\multirow{9}{*}{
\begin{tabular}{} \\ \end{tabular} } \\  & Corn & 891 & 77.91 & 87.65 & & 72.22 & 82.71 & & 18.61 & 95.17 & \\  & Cotton & 12 K & 78.22 & 96.13 & & 81.65 & 94.37 & & 34.52 & 90.69 & \\  & Sorghum & 242 & 59.99 & 100 & & 57.96 & 100 & & 24.31 & 85.95 & \\  & NCC & 892 & 61.11 & 99.77 & & 66.12 & 98.99 & & 18.68 & 66.92 & \\  & Pasture & 46 K & 84.98 & 89.85 & & 77.18 & 84.26 & & 56.56 & 86.87 & \\  & Eucalyptus & 17 K & **92.85** & 92.95 & & 93.31 & 90.88 & & 80.63 & 72.31 & \\  & Soil & 340 K & 87.28 & 87.3 & & 84.01 & 79.06 & & 71.88 & 62.55 & \\  & Grass & 112 & 29.71 & **100** & & 34.78 & 100 & & 63.15 & 80.35 & \\  & Cerrado & 17K & 84.43 & 91.49 & & 72.18 & 93.30 & & 55.73 & 81.94 & \\ \hline Average & - & - & 73.63 & 92.21 & 84.26 & 71.94 & 90.63 & 81.72 & 49.48 & 78.91 & 66.39 \\ \hline \multirow{9}{*}{Sequence 2} & Corn & 84 K & 61.96 & 80.22 & & 59.83 & 69.89 & & 47.81 & 70.33 & \\  & Cotton & 257 K & 87.43 & 94.96 & & 83.99 & 81.61 & & 80.01 & 71.48 & \\  & Sorghum & 6.3 K & 76.56 & 79.83 & & 62.91 & 75.87 & & 45.03 & 94.98 & \\  & NCC & 18 K & 78.63 & 87.63 & & 73.34 & 91.24 & & 63.86 & 88.15 & \\  & Pasture & 58 K & 89.15 & 89.79 & 77.32 & 82.45 & 84.73 & 76.94 & & 65.01 & 82.46 & 64.11 \\  & Eucalyptus & 17 K & **90.49** & 97.77 & & 83.34 & 97.70 & & **85.78** & 90.39 & \\  & Soil & 220 K & 65.54 & 50.01 & & 72.94 & 67.13 & & 52.93 & 40.94 & \\  & Grass & 112 & 65.68 & **100** & & 58.18 & **100** & & 40.01 & 85.71 & \\  & Cerrado & 17 K & 85.26 & 90.46 & & **85.93** & 87.38 & & 66.53 & 94.08 & \\ \hline Average & - & - & 77.86 & 85.63 & 77.32 & 73.66 & 83.95 & 76.94 & 60.78 & 79.84 & 64.11 \\ \hline \end{tabular}
\end{table} 
Table 4: The overall performance of the proposed DL architectures for patch classification tasks in terms of \(OA\), \(AA\), and \(f_{1}\) metrics.