## 5 Results
### Comparison Experiment Results  
To verify the overall performance advantages of the proposed PVM model, we conducted a comprehensive comparison with two representative time-series remote-sensing semantic segmentation methods--MTAF-TST and 2 DU-Net+CLSTM--along with a classic U-Net decoder structure. The experiments were independently trained and tested on the HN-NG Set dataset, and the classification results for some regions are shown in Figure 10. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**CCD Set**} \\  & **Precision (\%)** & **Recall (\%)** & **F1\_Score (\%)** & **IoU (\%)** & **mPA (\%)** \\ \hline PVM & 79.16 & 75.65 & 71.93 & 55.94 & 66.14 \\ VM & 72.38 & 70.13 & 66.99 & 52.37 & 61.29 \\ PV & 69.71 & 66.74 & 62.54 & 47.95 & 60.11 \\ V & 63.34 & 60.58 & 56.82 & 41.28 & 54.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiment results of PVM on the CCD dataset. In contrast, the 2 DU-Net+CLSTM model has performance bottlenecks when handling complex phenological backgrounds, with an F1-score of 69.36%, significantly lower than PVM, reflecting its relatively limited ability to model complex spatiotemporal evolution features. Furthermore, to analyze the impact of decoder design on model performance, we replaced the original PVM decoder with a classic U-Net decoder structure for comparison experiments. The results show that although this decoder still maintains good performance (F1-score of 71.25%), there is a 3.59% gap compared to the original decoder, indicating that the decoder module designed in this study, optimized based on the VIT feature structure, is better suited for the deep semantic features extracted by the Transformer. In contrast, the 2 DU-Net+CLSTM model exhibits performance bottlenecks when handling complex phenological backgrounds, with an F1-score of 69.36%, significantly lower than that of PVM. This reflects its relatively limited ability to model complex spatiotemporal evolution features. Furthermore, to analyze the impact of decoder design on model performance, we replaced the original PVM decoder with a classic U-Net decoder structure for comparison experiments. The results show that, although this decoder still maintains good performance (F1-score of 71.25%), there is a 3.59% gap compared to the original decoder, indicating that the decoder module designed in this study, optimized  
Figure 9: Distribution of attention weights across phenological stages for three non-food crops under different model variants (PAM, No-PAM, Static-Window). The rows correspond to canola, cotton, and tea, respectively. The columns represent the full model (with PAM), the No-PAM variant with temporal attention removed, and the Static-Window baseline with fixed temporal windows. The PAM curves exhibit sharp peaks during key phenological stages—Stage8–Stage9 (flowering stage) for canola, Stage11–Stage12 (boll stage) for cotton, and Stage7–Stage8/Stage10 (spring and summer flush) for tea—whereas both baselines show only broad or misaligned peaks. The gray shaded areas indicate the stages with the largest recall improvement (see Table 3), visually demonstrating that temporal attention enables the model to focus on the most discriminative phenological windows. based on the VIT feature structure, is better suited for the deep semantic features extracted by the Transformer. To further validate the generalization ability of the PVM model, we conducted additional comparison experiments on the CCD Set dataset. As shown in Table 5, despite facing more complex climatic conditions and crop distribution differences in this region, PVM still leads in all evaluation metrics, with an F1-score of 71.93% and IoU of 55.94%, which are 2.79% and 0.21% higher than the MTAF-TST model, respectively.