## 4 Conclusions  
To exploit the hierarchical label information of labels, this paper proposed an attention embedding, multi-task, multi-grained, network for scene classification, which used fine-grained labels and coarse-grained labels in different tasks and controlled the gradient  
Figure 11: Grad-CAM visualization results of (**a**) with the addition of the multi-grained label and (**b**) without the addition of multi-grained label relationships on the NWPU-RESISC45 dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **FLOPs(G)** \\ \hline VGG-16 [40] & 15.62 \\ \hline ResNet50 [16] & 4.13 \\ \hline T-CNN [46] & 4.67 \\ \hline Ours & 4.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: FLOPs (G) of different methods. propagation process of the coarse- and fine-grained classifiers. The proposed AEMMN can take advantage of hierarchical relationships to focus more on important features and better learn what is otherwise difficult for single granularity labels, thereby benefiting granularity classifiers and improving performance. The proposed AEMMN can be further extended to remote sensing image retrieval tasks. Image retrieval requires granularities with different labels under different conditions. In this case, the output label of the classifier corresponding to the granularity can be selected for use. In addition, the required granularity of the label can be related to the user's area of interest. The finer the label, the smaller the area of interest. Experiments were performed using the NWPU-RESISC45 dataset and the AID. We set the generated coarse-grained and fine-grained feature dimensions. In this experiment, the generated coarse-grained feature dimension was set to 100, and the fine-grained feature dimension was set to 500. The settings of fine-grained and coarse-grained feature dimensions not only needed to ensure that the fine-grained features would not lose information due to the low dimensions being too low, but they also needed to allow the coarse-grained features to occupy a certain proportion of the input features for the coarse-grained classifiers. In addition, the weights of the coarse- and fine-grained loss in the total loss were adjusted to determine how the model parameters were prioritized for calculation during coarse-grained tasks and fine-grained tasks. Through experiments, we have found that the proposed method can achieve a high level of performance when the coarse-grained loss weight is 1 and the fine-grained loss weight is 0.7. In future scene classification studies, we can improve the feature processing aspect of the coarse- and fine-grained module to make it more suitable for the corresponding classifier. Moreover, we can use simulated data technology to enrich our dataset. Simulated data can synthesize data pictures under different weather and lighting conditions. Simulated data can reduce the manual annotation of human resources and can enrich the dataset, which is beneficial to the performance of the model [48]. In addition, simulated data can also be used to validate experiments [49]. Conceptualization, P.Z., S.L., H.S. and D.Z. ; methodology, P.Z., S.L., H.S. and D.Z. ; software, P.Z. and S.L. ; validation, P.Z., H.S. and S.L. ; formal analysis, H.S. ; investigation, P.Z. and D.Z. ; resources, S.L. and D.Z. ; data curation, P.Z. and D.Z. ; writing--original draft preparation, P.Z. and S.L. ; writing--review and editing, P.Z., S.L., H.S. and D.Z. ; visualization, P.Z., S.L. and H.S. ; supervision, D.Z. and H.S. ; project administration, P.Z., S.L. and D.Z. ; funding acquisition, D.Z. and H.S. All authors have read and agreed to the published version of the manuscript. This research was supported in part by the National Natural Science Foundation of China (62177017, 41671377) and the Fundamental Research Funds for the Central Universities (KJ02502022-0169, CCNU22 QN014). Not applicable. Not applicable. Not applicable. Data Availability Statement: The data used to support the findings of this study are public image dataset [8; 37].