## 4 Experiments
### Experiment Results  
#### 4.3.1 Experiments between Images Collected by Satellites and UAVs  
Table 4 display the results of our proposed method (CAS-DA + filtering) in experiments between satellite images and UAV images. We can observe that our proposed method achieved 69.48%, 51.24%, 68.01%, and 87.06% in terms of F1-score on four transfer tasks; the average F1-score was 68.95%. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Dataset A** & **Dataset B** & **Dataset C** \\ \hline Training image & 3999 & 2950 & 3148 \\ Validation image & 819 & 832 & 851 \\ Labeled tree crown & 28,999 & 34,286 & 53,670 \\ Image size & 500 \(\times\) 500 & 500 \(\times\) 500 & 1024 \(\times\) 1024 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The necessary information on the datasets. We also compared our proposed method with other methods, including F-RCNN (source) [9], DA Faster R-CNN [39], Strong Weak Faster R-CNN [40], CROPTD [45], and SW-ICR-CCR [41]. The results are shown in Tables 5 and 6. F-RCNN (source) denotes Faster R-CNN trained only with source images and tested on the target images. Intuitively, a better performance of F-RCNN (source) implies a smaller gap in the feature distribution from the source domain to the target domain, i.e., a smaller domain shift. The other four are representative DA methods. For comparison fairness, the experiment setups of these methods were all as described in Section 4.2. In this paper, the bold values in the table indicate the maximum value in the corresponding evaluation index unless noted otherwise. Comparing the performance of these methods, a noteworthy phenomenon was the dramatic drop in the performance, especially the recall, of Strong Weak Faster R-CNN and CROPTD in B \(\rightarrow\) C, C \(\rightarrow\) B, and C \(\rightarrow\) A, with average F1-scores of only 19.13% and 35.67%, respectively. There are three possible key reasons for this: (1) As mentioned in Section 4.1, dataset C was the only dataset collected by UAV, which exhibited a clear visual difference with the other two datasets in terms of image quality/resolution, texture, etc. The knowledge learned by CAS-DA on image C could not be well generalized to image A and image B, which was proved by the poor results of Faster RCNN (source) on C \(\rightarrow\) B and C \(\rightarrow\) A. On the contrary, Faster RCNN (source) achieved good performance (even better than DA \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**A \(\rightarrow\) C**} & \multicolumn{3}{c}{**C \(\rightarrow\) A**} \\ \cline{2-7}
**Method** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline F-RCNN (source) & 63.35 & 59.42 & 61.49 & 6.55 & 3.50 & 4.56 \\ DA F-RCNN & 63.39 & 58.70 & 60.95 & 68.36 & 70.16 & 69.25 \\ Strong Weak Faster R-CNN & 63.55 & 61.29 & 62.40 & 26.19 & 30.80 & 28.31 \\ CROPTD & 65.82 & **63.84** & 64.81 & 47.76 & 49.02 & 48.38 \\ SW ICR-CCR & 63.92 & 59.44 & 61.60 & 70.34 & 72.79 & 71.54 \\
**CAS-DA + filtering** & **73.88** & 63.01 & **68.01** & **85.29** & **88.89** & **87.06** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The detection results of different methods on A \(\rightarrow\) C and C \(\rightarrow\) A. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **B \(\rightarrow\) C** & **C \(\rightarrow\) B** & **A \(\rightarrow\) C** & **C \(\rightarrow\) A** \\ \hline Labeled & 53,670 & 34,286 & 53,670 & 28,999 \\ Detected & 38,851 & 19,771 & 45,774 & 32,671 \\ TP & 31,916 & 12,359 & 27,865 & 27,865 \\ FP & 6935 & 7472 & 11,956 & 4806 \\ FN & 21,109 & 16,105 & 19,853 & 3483 \\ Precision/(\%) & 82.15 & 62.51 & 73.88 & 85.29 \\ Recall/(\%) & 60.19 & 43.42 & 63.01 & 88.89 \\ F1-score/(\%) & 69.48 & 51.24 & 68.01 & 87.06 \\ Average F1-score/(\%) & & 68.95 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The detection results of our proposed method in experiment between satellite images and UAV images.