# An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
## 4 Experiments and Analysis
### Experiments on Adaptive Weights versus Manual Weights  
In these experiments, we chose \(\alpha\) NCE + \(\beta\) RCE as a representative of the APL. Tables 1 and 2 display the retrieval performance of the CE, APL with 12 manual weights and our method on three different datasets with varying noise rates. The green and red fronts represent the lowest and highest retrieval performance of the 12 manually weighted combinations of \(\alpha\) NCE + \(\beta\) RCE, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Loss**} & \multicolumn{5}{c}{**The Noise Rate of UCMD**} \\ \cline{2-6}  & **Clean (0\%)** & **5.0\%** & **10.0\%** & **20.0\%** & **30\%** \\ \hline \hline CE & 97.09 \(\pm\) 0.37 & 94.37 \(\pm\) 0.02 & 89.81 \(\pm\) 0.40 & 78.89 \(\pm\) 0.87 & 74.58 \(\pm\) 2.99 \\ \hline
0.1 NCE + 0.1 RCE & 95.82 \(\pm\) 0.24 & 95.49 \(\pm\) 0.85 & 94.16 \(\pm\) 0.64 & 90.81 \(\pm\) 0.66 & 84.91 \(\pm\) 1.45 \\
0.1 NCE + 1 RCE & 96.63 \(\pm\) 0.24 & 95.12 \(\pm\) 1.02 & 93.59 \(\pm\) 1.00 & 91.01 \(\pm\) 0.66 & 85.79 \(\pm\) 0.91 \\
0.1 NCE + 10 RCE & 96.64 \(\pm\) 0.48 & 95.01 \(\pm\) 0.74 & 93.54 \(\pm\) 0.15 & 92.33 \(\pm\) 0.67 & 87.48 \(\pm\) 0.52 \\
0.1 NCE + 100 RCE & 96.66 \(\pm\) 0.39 & 94.01 \(\pm\) 0.73 2 & 94.13 \(\pm\) 0.96 & 89.91 \(\pm\) 0.67 2 & 84.14 \(\pm\) 1.04 2 \\
1 NCE + 0.1 RCE & 95.44 \(\pm\) 0.50 2 & 95.46 \(\pm\) 0.66 & 94.61 \(\pm\) 0.60 & 92.56 \(\pm\) 0.31 1 & 88.25 \(\pm\) 0.74 \\
1 NCE + 1 RCE & 95.95 \(\pm\) 0.37 & 95.63 \(\pm\) 0.17 & 94.18 \(\pm\) 0.39 & 90.12 \(\pm\) 0.50 & 86.17 \(\pm\) 0.90 \\
1 NCE + 10 RCE & 96.64 \(\pm\) 0.49 & 95.58 \(\pm\) 0.39 & 94.50 \(\pm\) 0.84 & 91.67 \(\pm\) 0.93 & 86.39 \(\pm\) 0.59 \\
1 NCE + 100 RCE & 96.80 \(\pm\) 0.45 & 95.33 \(\pm\) 0.98 & 94.56 \(\pm\) 0.45 & 91.74 \(\pm\) 0.29 & 87.90 \(\pm\) 0.68 \\
10 NCE + 0.1 RCE & 96.77 \(\pm\) 0.70 & 95.74 \(\pm\) 0.29 1 & 93.28 \(\pm\) 0.71 2 & 91.10 \(\pm\) 1.67 & 89.19 \(\pm\) 0.67 1 \\
10 NCE + 10 RCE & 96.82 \(\pm\) 0.59 1 & 95.65 \(\pm\) 0.42 & 94.79 \(\pm\) 0.71 1 & 92.43 \(\pm\) 1.26 & 89.15 \(\pm\) 0.68 \\
10 NCE + 10 RCE & 96.54 \(\pm\) 0.57 & 95.73 \(\pm\) 0.93 & 94.63 \(\pm\) 0.79 & 91.23 \(\pm\) 1.19 & 87.42 \(\pm\) 1.69 \\
10 NCE + 100 RCE & 96.47 \(\pm\) 0.57 & 94.78 \(\pm\) 0.61 & 94.33 \(\pm\) 0.72 & 91.28 \(\pm\) 1.45 & 86.44 \(\pm\) 0.99 \\ \hline A-NCE + RCE (ours) & 97.00 \(\pm\) 0.87 * & 96.51 \(\pm\) 0.40 * & 95.02 \(\pm\) 0.51 * & 93.00 \(\pm\) 0.32 * & 90.01 \(\pm\) 0.88 * \\ \hline \hline \end{tabular}  
* * represents the highest retrieval precision at the same noise rate, and superscripts 1 and 2 represent the highest and lowest retrieval performance of the 12 manually weighted combinations of a NCE + \(\beta\) RCE, respectively.  
\end{table} 
Table 1: mAPs (%) (mean \(\pm\) standard deviation) on the UCMD dataset for adaptive weights versus manual weights. Figure 4: Examples of the NWPU dataset: (1) airplane; (2) airport; (3) baseball diamond; (4) basketball court; (5) beach; (6) bridge; (7) chaparral; (8) church; (9) circular farmland; (10) cloud; (11) commercial area; (12) dense residential; (13) desert; (14) forest; (15) freeway; (16) golf course; (17) ground track field; (18) harbour; (19) industrial area; (20) intersection; (21) island; (22) lake; (23) meadow; (24) medium residential; (25) mobile home park; (26) mountain; (27) overpass; (28) palace; (29) parking lot; (30) railway; (31) railway station; (32) rectangular farmland; (33) river; (34) roundabout; (35) runway; (36) sea ice; (37) ship; (38) snowberg; (39) sparse residential; (40) stadium; (41) storage tank; (42) tennis court; (43) terrace; (44) thermal power station; (45) wetland. It can be seen from Tables 1-3 that the original APL method outperforms the traditional CE method in the presence of noisy labels. However, the weighting hyperparameters required for optimal performance vary across the three datasets and noise rates. This means that, as the dataset and noise rate change, the original APL method must be repeatedly tuned. This is a time-consuming process with uncertain results.