# Generative Adversarial Networks for Zero-Shot Remote Sensing Scene Classification
## 3 Results
### Comparison with State-of-the-Art
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **35/10** & **30/15** & **25/20** & **20/25** \\ \hline SSE [10] & 33.36 \(\pm\) 3.58 & 23.30 \(\pm\) 2.48 & 16.88 \(\pm\) 2.29 & 12.94 \(\pm\) 1.46 \\ DMaP [12] & 49.53 \(\pm\) 6.31 & 38.07 \(\pm\) 4.83 & 28.15 \(\pm\) 3.86 & 23.95 \(\pm\) 2.60 \\ SAE [36] & 44.81 \(\pm\) 4.73 & 35.07 \(\pm\) 3.91 & 24.65 \(\pm\) 3.71 & 20.77 \(\pm\) 2.02 \\ ZSL-LP [19] & 47.00 \(\pm\) 6.64 & 36.45 \(\pm\) 4.58 & 26.71 \(\pm\) 3.43 & 22.90 \(\pm\) 2.47 \\ ZSC-SA [20] & 48.40 \(\pm\) 6.36 & 37.55 \(\pm\) 4.54 & 28.27 \(\pm\) 3.47 & 23.69 \(\pm\) 2.38 \\ VSC [37] & 50.68 \(\pm\) 6.60 & 40.92 \(\pm\) 4.59 & 30.62 \(\pm\) 3.10 & 25.51 \(\pm\) 2.04 \\ VSOP [38] & 45.32 \(\pm\) 5.71 & 36.09 \(\pm\) 4.63 & 25.44 \(\pm\) 3.13 & 22.18 \(\pm\) 2.00 \\ f-CLSWGAN [14] & 45.35 \(\pm\) 6.37 & 38.97 \(\pm\) 4.93 & 30.06 \(\pm\) 2.96 & 24.31 \(\pm\) 2.57 \\ CYCLEWGAN [16] & 46.87 \(\pm\) 5.99 & 39.85 \(\pm\) 4.71 & 31.17 \(\pm\) 2.66 & 25.06 \(\pm\) 2.74 \\ RBGN [39] & 44.68 \(\pm\) 6.14 & 40.31 \(\pm\) 4.89 & 31.91 \(\pm\) 3.07 & 24.89 \(\pm\) 2.44 \\ DSAE [21] & **51.52 \(\pm\) 6.91** & **41.94 \(\pm\) 4.61** & 31.85 \(\pm\) 3.32 & 25.20 \(\pm\) 2.17 \\ CSPWGAN (our) & 50.66 \(\pm\) 5.86 & 41.61 \(\pm\) 4.48 & **32.09 \(\pm\) 2.96** & **26.65 \(\pm\) 2.33** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: With different seen/unseen ratios, the average ZSL classification accuracies and standard deviation (%) of our CSPWGAN model and the sota methods on the NWPU45 dataset. Bold font denotes the best result. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **25/5** & **20/10** & **15/15** & **10/20** \\ \hline SSE [10] & 46.11 \(\pm\) 7.21 & 30.28 \(\pm\) 4.90 & 19.94 \(\pm\) 2.43 & 12.73 \(\pm\) 1.27 \\ DMaP [12] & 43.40 \(\pm\) 7.29 & 28.29 \(\pm\) 4.78 & 19.38 \(\pm\) 2.62 & 11.56 \(\pm\) 1.29 \\ SAE [36] & 47.34 \(\pm\) 8.42 & 32.12 \(\pm\) 4.45 & 23.73 \(\pm\) 3.28 & 13.77 \(\pm\) 1.17 \\ ZSL-LP [19] & 46.77 \(\pm\) 7.65 & 30.82 \(\pm\) 4.90 & 21.78 \(\pm\) 3.37 & 12.97 \(\pm\) 1.06 \\ ZSC-SA [20] & 50.87 \(\pm\) 8.74 & 33.46 \(\pm\) 5.99 & 24.41 \(\pm\) 3.83 & 15.89 \(\pm\) 2.03 \\ VSC [37] & 52.61 \(\pm\) 8.37 & 35.85 \(\pm\) 5.52 & 26.11 \(\pm\) 3.76 & 17.50 \(\pm\) 2.19 \\ VSOP [38] & 48.56 \(\pm\) 7.90 & 32.95 \(\pm\) 5.52 & 24.84 \(\pm\) 3.04 & 14.03 \(\pm\) 2.47 \\ f-CLSWGAN [14] & 50.68 \(\pm\) 11.25 & 33.89 \(\pm\) 5.72 & 24.95 \(\pm\) 2.96 & 17.26 \(\pm\) 3.06 \\ CYCLEWGAN [16] & 52.37 \(\pm\) 10.47 & 35.94 \(\pm\) 5.46 & 25.28 \(\pm\) 2.66 & 17.89 \(\pm\) 2.86 \\ RBGN [39] & 51.99 \(\pm\) 11.32 & 36.27 \(\pm\) 5.65 & 24.83 \(\pm\) 3.07 & 16.83 \(\pm\) 3.14 \\ DSAE [21] & 53.49 \(\pm\) 8.58 & 35.32 \(\pm\) 5.17 & 25.92 \(\pm\) 3.92 & 17.65 \(\pm\) 2.52 \\ CSPWGAN (our) & **55.86 \(\pm\) 10.60** & **37.93 \(\pm\) 5.26** & **26.97 \(\pm\) 2.53** & **19.43 \(\pm\) 3.02** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: With different seen/unseen ratios, the average ZSL classification accuracies and standard deviation (%) of our CSPWGAN model and the sota methods on the AID30 dataset. Bold font denotes the best result. \"basketball court\", \"circular farmland\", \"cloud\", \"dense residential\", \"desert\",\"harbor\", \"intersection\", \"medium residential\" and \"sparse residential\". Figure 6 shows the confusion matrix of the DASE method and our method on the NWPU45 datasets. We achieved 2%, 5%, 60%, 16%, and 1% improvement on the classes \"airport\", \"circular farmland\", \"dense residential\", \"medium residential\" and \"sparse residential\", respectively. To visually display the efficiency of CSPWGAN, we visualized the synthesized image features through our CSPWGAN method and the original image features of the unseen class in Figure 7. We used the popular t-SNE [40] tool to display the synthesized image features and the real image features. From Figure 7, we can observe that the unseen image features synthesized by our method are relatively close to the real image feature distribution, which proves that our approach is meaningful.