# An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
## 5 Conclusions  
In this paper, we propose an adaptively weighted method based on active passive loss (APL) for remote sensing image retrieval. To automatically determine the weight hyperparameters of active losses and passive losses of different samples, we first design or select two metrics to measure the sample complexity and noise level based on entropy, standard deviation and predicted probability difference. Then, an adaptive weighted learning network (AWNet) based on the multilayer perceptron is designed to automatically predict the weighting parameters.  
In order to verify the effectiveness and portability of our method, four groups of experiments are designed. First of all, the experimental results show that the retrieval accuracy of our method is better than that of 12 manual weighting combinations on three datasets: UCMD, AID and NWPU with five noise rates. Secondly, compared with seven other state-of-the-art robust losses, our method achieves the best performance, and the mAPs are improved by 0.4% to 2.56%. In the third group of experiments, we compare the retrieval accuracy and model complexity of our method using six different backbones. The results show that our method has excellent performance and good portability without increasing the computational cost too much. Finally, we proved the rationality of the AWNet's input metrics and structure through several groups of ablation experiments. In addition, the results show that our method achieves better image classification accuracy than manual weighting. Therefore, our process of adaptively learning the weighting parameters can benefit other areas such as image classification and segmentation with noisy labels.  
Although the existing retrieval models have achieved excellent retrieval performance on single-domain datasets, they are difficult to generalise to test datasets in other domains. To address this issue, many scholars have proposed remote sensing image retrieval methods that enhance the generalisation performance of models on different data sources. For example, [PERSON] et al. [4] implemented unsupervised cross-domain remote sensing image retrieval by using pseudo-label self-training and consistency regulation. However, those domain adaptation methods lack research on noise labels. Therefore, our future work will be concentrated on the effect of noise samples on domain adaptation remote sensing image retrieval.  
Conceptualisation, X.T., D.H., S.W. and H.X.; methodology, X.T., D.H. and X.L.; validation, X.T.; writing--original draft preparation, X.T., D.H. and H.X.; funding acquisition, D.H. and H.X. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the National Natural Science Foundation of China under grant 42171457, in part by the Shandong Province Excellent Youth Fund under grant ZR2022 YQ36 and in part by the Yunnan Provincial Philosophy and Social Science Innovation Team Construction Project under grant 2023 CX02.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{4}{c}{**The Number of Hidden Layers**} \\ \hline
**The Number of Neurons in Each** & & & \\
**Hidden Layer** & **1** & **2** & **3** \\ \hline
50 & 90.65 \(\pm\) 0.72 & 91.99 \(\pm\) 0.75 & 91.37 \(\pm\) 0.97 \\
100 & 93.00 \(\pm\) 0.32 * & 91.41 \(\pm\) 0.27 & 92.06 \(\pm\) 0.18 \\ \hline \hline \end{tabular}  
* Represents the highest retrieval precision.  
\end{table}
Table 10: mAPs (%) (mean \(\pm\) standard deviation) of different numbers of hidden layers on the UCMD dataset with 20% noise.