# 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** & **16/5** & **13/8** & **10/11** & **7/14** \\ \hline SSE [10] & 35.59 \(\pm\) 5.90 & 23.42 \(\pm\) 3.81 & 17.07 \(\pm\) 3.56 & 10.82 \(\pm\) 2.10 \\ DMaP [12] & 48.92 \(\pm\) 8.71 & 30.91 \(\pm\) 4.77 & 22.99 \(\pm\) 4.81 & 17.30 \(\pm\) 3.04 \\ SAE [36] & 49.50 \(\pm\) 8.42 & 32.71 \(\pm\) 6.49 & 24.04 \(\pm\) 4.36 & 18.63 \(\pm\) 2.76 \\ ZSL-LP [19] & 49.01 \(\pm\) 8.85 & 31.26 \(\pm\) 5.09 & 23.28 \(\pm\) 4.13 & 17.55 \(\pm\) 2.90 \\ ZSC-SA [20] & 50.42 \(\pm\) 8.84 & 34.12 \(\pm\) 6.10 & 24.68 \(\pm\) 4.22 & 18.38 \(\pm\) 2.74 \\ VSC [37] & 55.91 \(\pm\) 11.77 & 36.26 \(\pm\) 7.31 & 25.97 \(\pm\) 5.79 & 19.53 \(\pm\) 3.05 \\ VSOP [38] & 46.48 \(\pm\) 7.83 & 29.81 \(\pm\) 4.56 & 21.97 \(\pm\) 4.11 & 16.14 \(\pm\) 2.59 \\ f-CLSWGAN [14] & 56.97 \(\pm\) 11.06 & 36.47 \(\pm\) 6.28 & 27.89 \(\pm\) 4.99 & 19.34 \(\pm\) 3.96 \\ CYCLEWGAN [16] & 58.36 \(\pm\) 10.04 & 36.81 \(\pm\) 5.53 & 28.37 \(\pm\) 4.53 & 21.15 \(\pm\) 3.51 \\ RBGN [39] & 57.93 \(\pm\) 11.56 & 36.95 \(\pm\) 5.99 & 27.74 \(\pm\) 5.16 & 20.67 \(\pm\) 3.95 \\ DSAE [21] & 58.63 \(\pm\) 11.23 & 37.50 \(\pm\) 7.79 & 25.59 \(\pm\) 5.24 & 20.18 \(\pm\) 3.07 \\ CSPWGAN (our) & **62.66 \(\pm\) 10.79** & **46.19 \(\pm\) 5.52** & **35.17 \(\pm\) 4.93** & **26.17 \(\pm\) 3.87** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: With different seen/unseen ratios, the average ZSL classification accuracies and standard deviation (%) of our CSPWGAN model and the sota methods on the UCM21 dataset. Bold font denotes the best result. To compare the classification performance for each unseen category, we fixed the unseen classes for a given split ratio according to the DSAE setting. We present the confusion matrix of our approach and the DASE method. Notably, each column of the matrix represents an instance of the real class, and each row of the matrix represents an instance of the predicted class. On the UCM21 dataset, we have five unseen classes with the seen/unseen ratio set to 16/5, i.e., \"freeway\", \"golf course\", \"intersection\", \"medium residential\", and \"storage tanks\". Figure 4 shows the confusion matrix of the DASE method and our method on the UCM21 datasets. We found that we obtained 27% and 22% improvement on the classes \"freeway\" and \"storage tanks\", respectively. Our method has a decrease in classification accuracy on other unseen classes, but the average accuracy of the five unseen classes is higher than DASE. The average accuracy of our method improved from 63.6% to 68.8% on the five unseen categories. On the AID30 dataset, we have five unseen classes with the seen/unseen ratio set to 25/5, i.e., \"dense residential\", \"desert\", \"forest\", \"industrial\", and \"pond\". Figure 5 shows the confusion matrix of the DASE method and our method on the AID30 datasets. We achieved 31% and 80% improvement on the classes \"dense residential\" and \"forest\", respectively. The average accuracy of our method improved from 64.8% to 72.4% on the five unseen categories. On the NWPU45 dataset, we have ten unseen classes with the seen/unseen ratio set to 35/10, i.e., \"airport\", \"