# Deep Unsupervised Embedding for Remote Sensing Image Retrieval Using Textual Cues
## 4 Conclusions  
In this work, we have proposed an unsupervised learning method for image retrieval in remote sensing imagery. Unlike traditional remote sensing image-to-image retrieval, this approach addresses the problem of text-to-image retrieval. The network consists of two asymmetric branches for image and sentence encoding, respectively. The experiments provided on two different benchmark datasets show interesting results compared to a recent method based on triplet networks. For future developments, we propose to investigate other embedding models in addition to more robust losses to increase the retrieval accuracy.  
[PERSON] and [PERSON] designed and implemented the method, and wrote the paper. [PERSON], [PERSON] and [PERSON] contributed to the analysis of the experimental results and paper writing. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Pre-Trained**} & \multirow{2}{*}{**Hidden Layer Size**} & \multicolumn{3}{c}{**Text-to-Image**} & \multicolumn{3}{c}{**Image-to-Text**} \\ \cline{3-8}  & & **R@1** & **R@5** & **R@10** & **R@1** & **R@5** & **R@10** \\ \hline ImageNet-21k & 256 & 22.95 & 59.52 & 77.23 & 25.47 & 59.76 & 72.61 \\ m-R50x1 & 512 & 22.76 & 58.47 & 78.38 & 25.00 & 56.66 & 70.47 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Retrieval results on the Merced dataset using two different configurations for the hidden layer.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Pre-Trained**} & \multirow{2}{*}{**Architecture**} & \multicolumn{3}{c}{**Text-to-Image**} & \multicolumn{3}{c}{**Image-to-Text**} \\ \cline{3-6}  & & **R@1** & **R@5** & **R@10** & **R@1** & **R@5** & **R@10** \\ \hline \multirow{3}{*}{ImageNet-21k} & m-R50x1 & 22.95 & 59.52 & 77.23 & 25.47 & 59.76 & 72.61 \\  & m-R50x3 & 23.23 & 58.95 & 77.00 & 25.47 & 59.76 & 72.61 \\  & m-R10x1 & 23.68 & 60.38 & 78.52 & 21.19 & 56.90 & 75.23 \\ \hline ImageNet-1k & s-R50x1 & 19.71 & 56.04 & 74.76 & 21.66 & 53.57 & 72.82 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Text-to-image and image-to-text retrieval results on the Merced dataset.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Pre-Trained**} & \multirow{2}{*}{**Hidden Layer Size**} & \multicolumn{3}{c}{**Text-to-Image**} & \multicolumn{3}{c}{**Image-to-Text**} \\ \cline{3-6}  & & **R@1** & **R@5** & **R@10** & **R@1** & **R@5** & **R@10** \\ \hline ImageNet-21k & 256 & 19.02 & 55.25 & 77.23 & 21.86 & 60.00 & 75.58 \\ m-R50x1 & 512 & 18.18 & 52.10 & 67.53 & 21.86 & 54.88 & 73.25 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Retrieval results on the TextRS dataset using two different configurations for the hidden layer.  
**Funding:** This work was supported by the Deanship of Scientific Research at King Saud University through the Local Research Group Program under Project RG-1435-050.  
**Acknowledgments:** This work was supported by the Deanship of Scientific Research at King Saud University through the Local Research Group Program under Project RG-1435-050.  
**Conflicts of Interest:** The authors declare no conflict of interest.