# Crop Land Change Detection with MC&N-PSPNet
## 3 Results
### Control Experiment
However, the edges of the extraction results of the method in this paper also have a slight amount of jaggedness. A loophole in labeling is also found when comparing the results of multiple groups of methods, such as in Detailed Figure 1: each method determines the parcel in the upper-left corner as an agricultural area, and reviewing the original figure reveals that there is indeed a problem with visual interpretation, and the subsequent semi-supervised or unsupervised approach will be used to complete the work of producing labels to eliminate the influence of subjectivity. This finding also laterally certifies the efficient accuracy of the model. As can be seen from Table 1, the production accuracy of this paper's method in the agricultural area, the user accuracy in the non-agricultural area, and the MioU, MPA, overall accuracy, and Kappa coefficient are all higher than those of other methods. The overall accuracy is 95.03% and the Kappa coefficient is 0.8815, which indicates that the results of this paper have a high degree of consistency with the actual distribution of the landforms. In order to verify the efficiency of the attention module used in this paper, the NAM module is replaced with CBAM, SENet, ECA, and BAM modules in the same location in turn for comparison tests, and the accuracy assessment results are shown in Table 2. The accuracy assessment results show that the production accuracy and user accuracy of agricultural and non-agricultural areas, as well as the overall six evaluation accuracies of MioU, MPA, OA, and Kappa value, are higher than the other methods, which corroborates that the addition of NAM is the most suitable for the network model structure in this paper. The MC&N-PSPNet proposed in this paper introduces the CBAM module into the MobileNetV2 backbone feature extraction network, enhancing the model's ability to resolve agricultural areas in remote sensing images. This improvement aids in distinguishing between agricultural and non-agricultural areas that may exhibit similarities, leading to greater efficiency, albeit with some loss in effectiveness. Furthermore, the introduction of the NAM module after feature fusion helps the model to capture richer and more distinctive feature information at different levels, facilitating the detection of agricultural areas with varying features due to differences in planting patterns and crop types. The agricultural area producer accuracy, non-agricultural area user accuracy, overall accuracy \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature Type**} & \multicolumn{2}{c}{**CBAM**} & \multicolumn{2}{c}{**SENet**} & \multicolumn{2}{c}{**ECA**} & \multicolumn{2}{c}{**BAM**} & \multicolumn{2}{c}{**MC\&N**} \\  & **UA\%** & **PA\%** & **UA\%** & **PA\%** & **UA\%** & **PA\%** & **UA\%** & **UA\%** & **PA\%** \\ \hline Agricultural Area & 97.27 & 92.07 & 94.56 & 92.39 & 96.67 & 92.39 & 95.27 & 92.71 & **97.36** & **95.58** \\ Non-Agricultural Area & 81.08 & 93.63 & 82.48 & 87.09 & 82.10 & 91.63 & 83.14 & 88.64 & **89.74** & **93.72** \\ MIoU & 83.57 & 80.61 & & 82.95 & & 81.88 & & **88.90** \\ MPA & 89.37 & & 88.52 & & 89.38 & 89.20 & & **93.55** \\ OA\% & 92.56 & & 90.84 & & 92.18 & 91.53 & & **95.03** \\ Kappa & 81.86 & & 0.7819 & & 0.8110 & & 0.7978 & & **0.8815** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the accuracy of the extraction results for different attention modules for agricultural areas. (OA), and Kappa values achieved by this method surpass those of other methods. The lower agricultural area user accuracy is attributed to significant differences in spectral information features among different crops, leading to potential misclassification. Experimental results demonstrate that the network model proposed in this paper achieves an overall accuracy of 95.03% and a Kappa coefficient of 0.8815 on the HRSCD dataset, indicating a high level of consistency with the actual distribution of land features.