## 4 Experiments
### Experiment Results
#### 4.3.1 Experiments between Images Collected by Satellites and UAVs
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**B \(\rightarrow\) C**} & \multicolumn{3}{c}{**C \(\rightarrow\) B**} \\ \cline{2-7}
**Method** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline F-RCNN (source) & 49.97 & 0.22 & 0.43 & 1.14 & 0.57 & 0.76 \\ DA F-RCNN & **90.36** & 46.77 & 61.64 & 30.48 & **44.90** & 36.45 \\ Strong Weak Faster R-CNN & 26.86 & 14.45 & 18.79 & 23.53 & 6.59 & 10.30 \\ CROPTD & 58.07 & 28.02 & 37.80 & 47.58 & 13.32 & 20.82 \\ SW ICR-CCR & 85.55 & 55.62 & 67.41 & 39.89 & 19.06 & 25.80 \\
**CAS-DA + filtering** & 82.15 & **60.19** & **69.48** & **62.51** & 43.42 & **51.24** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The detection results of different methods on B \(\rightarrow\) C and C \(\rightarrow\) B.  
F-RCNN) on A \(\rightarrow\) C, indicating that the knowledge learned in dataset A could be directly transferred to dataset C to a certain extent. (2) In image C, there were palms in unhealthy growth status such as dead and yellowed ones that did not exist in image A and image B. Consequently, when C was taken as the target domain, these unhealthy palms were missed by the detector due to their difference from healthy ones in the source domain in terms of texture, size, etc., resulting in a decrease in recall. When we consider C as the source domain, these outlier features were prone to negative transfer, hurting the transferability of the model. (3) CROPTD is inherited from Strong Weak Faster R-CNN. Compared to other DA methods, both eliminated instance-level alignment, which also caused a performance decline, especially when there were a quantity of background samples similar to the object. Our proposed method ranked at or near the top of the listed DA methods in terms of both precision and recall, thus achieving the highest F1-score in all four transfer experiments. Compared with Strong Weak Faster R-CNN (baseline), our method showed a greater robustness and improved the F1-score on the four experiments by 5.61%-58.75%. To demonstrate the experimental results more clearly, Table 7 shows the F1-scores of all methods in the four experiments. Our proposed method achieved the best performance with 68.95% in terms of average F1-score, outperforming DA F-RCNN, Strong Weak Faster R-CNN, CROPTD, and SW ICR-CCR by an obvious margin of 11.88%, 40.00%, 12.36%, and 26.01%, respectively. We divided the experimental results from Table 7 into two groups: adaptation between dataset B and dataset C (i.e., B\(\leftrightarrow\)C, including B \(\rightarrow\) C and C \(\rightarrow\) B), and adaptation between dataset A and dataset C (i.e., A\(\leftrightarrow\) C, including A \(\rightarrow\) C and C \(\rightarrow\) A). We noticed that the performance of Faster RCNN (source) in B\(\leftrightarrow\) C was clearly worse than that in A\(\leftrightarrow\) C. Combined with the previously mentioned results, we can draw the conclusion that the domain shift between B and C was more significant than that between A and C, which was also confirmed by the experimental results of the DA methods: taking C as the source domain, in C \(\rightarrow\) B and C \(\rightarrow\) A, all of the DA methods obtained the worst performance in C \(\rightarrow\) B, and our method also achieved an F1-score of only 51.24%, while in C \(\rightarrow\) A, they performed much better, and our method achieved an F1-score of 87.06%. Analyzing these two groups of experiments from another perspective, it can be seen that our method led to a more remarkable improvement of the adaptation with a larger domain shift. Specifically, compared with the other four DA methods, our method improved the average F1-score of B\(\leftrightarrow\)C by 11.32%-45.82%, exceeding 10.97-32.18% on A\(\leftrightarrow\)C. It proved that our method, which focuses on improving classification capability on source domain, was more effective than the other DA methods that focus on diminishing the domain shift across common benchmarks, especially in the presence of a large domain shift.