# Crop Land Change Detection with MC&N-PSPNet
## 3 Results
### Control Experiment  
To verify the effectiveness of the semantic segmentation model proposed in this paper, it is compared with the results of U-Net, DeepLabV3+ [25], and proposed methods with those reported in previous work [26]. Some of the results are shown in Figure 8. In this paper, the performance of different network models is quantitatively evaluated using six evaluation metrics such as average crossover and merge rate, average pixel accuracy, user accuracy, producer accuracy, overall accuracy, and Kappa coefficient. The results are shown in Table 1.  
Figure 7: Extraction results of the method in this paper: (**a**) original figures; (**b**) 2006 ground truth; (**c**) 2012 ground truth; (**d**) detailed drawings (from top to bottom: original, label, result of MC&N-PSPNet). As can be seen in Figure 8, the agricultural area extracted by the original network is incomplete, and the boundary is not clear. The layers of the ResNet50 backbone feature extraction network used by PSPNet are deeper, the internal differences of each wetland type in the test area are small, and the advantages of the deep network structure cannot be expressed, which causes the agricultural area extraction results to have the phenomena of sticking and fragmentation, as shown in Figure 8c. The extraction of the agricultural area with simple edges is performed well by U-Net, but when faced with complex edges, the edges are blurred and inaccurate, and the training time is the longest among all the methods. U-Net has a good effect for the extraction of agricultural areas with simple edges, but when facing agricultural areas with complex edges, the edges are fuzzy and inaccurate, there is the phenomenon of missed extraction, and the training time is the \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature Type**} & \multirow{2}{*}{**Original Network**} & \multirow{2}{*}{**U-Net**} & \multirow{2}{*}{**Literature [26]**} & \multirow{2}{*}{**DeepLabV3++**} & \multicolumn{2}{c}{**MC&N**} \\  & & & & & & & & **-PSPsNet.** \\ \cline{2-11}  & **UA\%** & **PA\%** & **UA\%** & **PA\%** & **UA\%** & **PA\%** & **UA\%** & **PA\%** & **UA\%** & **PA\%** \\ \hline Agricultural Area & 97.26 & 91.95 & 96.79 & 91.97 & 97.20 & 95.48 & **99.03** & 87.01 & 97.36 & **95.58** \\ Non-Agricultural Area & 80.59 & 93.05 & 80.09 & 91.65 & 89.52 & 93.35 & 66.34 & **96.79** & **89.74** & 93.72 \\ MIoU & 82.87 & 81.84 & 88.55 & 75.60 & **88.90** \\ MPA & 88.97 & 88.45 & 93.37 & 82.69 & **93.55** \\ OA\% & 92.24 & 91.70 & 93.86 & 89.06 & **95.03** \\ Kappa & 0.8099 & 0.7971 & 0.8674 & 0.7170 & **0.8815** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the precision of extraction results of different methods in agricultural areas. Figure 8: Extraction results of different model agricultural areas: (**a**) original figures; (**b**) ground truth; (**c**) original network; (**d**) U-Net; (**e**) DeepLabV3++; (**f**) literature [26] (**g**) MC&N-PSPsNet. longest among all methods. As for the DeepLabV3+ classification confusion phenomenon, the extraction results have an obvious \"pretzel phenomenon\" that is serious, wherein a small field is part of an incorrect mention: the non-agricultural area being part of the classification for the agricultural area. In the literature [26], through the introduction of the SENet module for classification before PSPNet feature extraction, the method applied in the HRSC dataset can only be extracted from the general outline because the boundary of the misclassification is serious and a small number of missed points will occur. This paper's method effectively reduces the misclassification phenomenon in the extraction process of agricultural areas by superimposing the results of pyramid pooling, improving the correct rate of information, and improving the accuracy and robustness of the model without increasing the complexity of the model through the attention mechanism NAM, which improves the extraction of agricultural areas.