# An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
## 4 Experiments and Analysis
### Experiments on Adaptive Weights versus Manual Weights
Furthermore, it can also be seen that our method achieves the best performance in the three datasets at different noise rates. This indicates that our adaptive weighted method is effective for the robust APL method. In Table 4, we compare the image classification accuracy between adaptive weights and manual weights. It is evident that our method is more conducive to improving the feature extraction ability of the model and achieves higher image classification accuracy compared to manual weights. Therefore, our method is also suitable for image-level computer vision tasks based on high-level features such as image classification. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Loss**} & \multicolumn{5}{c}{**The Noise Rate of AID**} \\ \cline{2-6}  & **Clean (0\%)** & **5.0\%** & **10.0\%** & **20.0\%** & **30\%** \\ \hline CE & 93.17 \(\pm\) 0.94 & 90.07 \(\pm\) 0.39 & 84.15 \(\pm\) 0.46 & 71.95 \(\pm\) 0.15 & 69.45 \(\pm\) 3.41 \\ \hline
0.1 NCE + 0.1 RCE & 92.58 \(\pm\) 0.62 & 92.06 \(\pm\) 0.40 & 90.90 \(\pm\) 0.48 & 88.38 \(\pm\) 0.18 & 85.99 \(\pm\) 0.61 \\
0.1 NCE + 1 RCE & 92.89 \(\pm\) 0.48 & 92.13 \(\pm\) 0.27 & 91.30 \(\pm\) 0.22 \({}^{1}\) & 88.06 \(\pm\) 0.47 & 85.71 \(\pm\) 0.32 \\
0.1 NCE + 10 RCE & 92.34 \(\pm\) 0.44 & 91.69 \(\pm\) 0.22 & 90.82 \(\pm\) 0.28 & 88.63 \(\pm\) 0.30 & 84.79 \(\pm\) 0.82 \\
0.1 NCE + 10 ROC & 92.93 \(\pm\) 0.42 & 91.91 \(\pm\) 0.21 & 89.80 \(\pm\) 0.46 \({}^{2}\) & 88.34 \(\pm\) 0.63 & 84.28 \(\pm\) 0.20 \({}^{2}\) \\
1 NCE + 0.1 RCE & 92.55 \(\pm\) 0.27 & 92.43 \(\pm\) 0.11 \({}^{1}\) & 91.01 \(\pm\) 0.44 & 89.74 \(\pm\) 0.43 & 86.94 \(\pm\) 0.37 \({}^{1}\) \\
1 NCE + 1 0 RCE & 92.39 \(\pm\) 0.32 & 91.98 \(\pm\) 0.48 & 91.05 \(\pm\) 0.53 & 89.37 \(\pm\) 0.36 & 86.53 \(\pm\) 0.11 \\
1 NCE + 10 RCE & 91.95 \(\pm\) 0.30 & 92.36 \(\pm\) 0.32 & 91.25 \(\pm\) 0.33 & 89.06 \(\pm\) 0.75 & 85.50 \(\pm\) 0.32 \\
1 NCE + 100 RCE & 92.60 \(\pm\) 0.26 & 92.38 \(\pm\) 0.57 & 90.93 \(\pm\) 0.54 & 87.85 \(\pm\) 0.88 \({}^{2}\) & 85.51 \(\pm\) 0.63 \\
10 NCE + 0.1 RCE & 93.25 \(\pm\) 0.17 & 92.41 \(\pm\) 0.75 & 90.26 \(\pm\) 0.30 & 89.30 \(\pm\) 0.99 & 86.78 \(\pm\) 1.57 \\
10 NCE + 1 0 RCE & 92.76 \(\pm\) 0.31 & 92.27 \(\pm\) 0.12 & 90.89 \(\pm\) 0.13 & 89.79 \(\pm\) 0.68 \({}^{1}\) & 86.53 \(\pm\) 0.35 \\
10 NCE + 10 RCE & 92.94 \(\pm\) 0.32 & 92.40 \(\pm\) 0.11 & 91.08 \(\pm\) 0.12 & 89.66 \(\pm\) 0.30 & 86.63 \(\pm\) 0.30 \\
10 NCE + 100 RCE & 92.89 \(\pm\) 0.33 & 91.58 \(\pm\) 0.13 \({}^{2}\) & 91.15 \(\pm\) 0.35 & 88.48 \(\pm\) 0.40 & 85.14 \(\pm\) 0.98 \\ \hline A-NCE + RCE (ours) & 93.68 \(\pm\) 0.38 \({}^{*}\) & 92.74 \(\pm\) 0.05 \({}^{*}\) & 92.37 \(\pm\) 0.22 \({}^{*}\) & 90.22 \(\pm\) 0.70 \({}^{*}\) & 87.17 \(\pm\) 0.54 \({}^{*}\) \\ \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 2: mAPs (%) (mean \(\pm\) standard deviation) on the AID dataset for adaptive weights versus manual weights. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Loss**} & \multicolumn{5}{c}{**The Noise Rate of NWPU**} \\ \cline{2-6}  & **Clean (0\%)** & **5.0\%** & **10.0\%** & **20.0\%** & **30\%** \\ \hline CE & 90.99 \(\pm\) 0.75 & 85.96 \(\pm\) 0.82 & 81.57 \(\pm\) 1.84 & 80.41 \(\pm\) 0.52 & 77.07 \(\pm\) 0.03 \\ \hline
0.1 NCE + 0.1 RCE & 89.55 \(\pm\) 0.42 & 89.47 \(\pm\) 0.53 & 87.92 \(\pm\) 0.63 &