# Generative Adversarial Networks for Zero-Shot Remote Sensing Scene Classification
## 3 Results
### Comparison with State-of-the-Art  
To show the superiority of CSPWGAN, we present a comparison of our method with some previous methods. We chose these baselines: SSE [10], DMaP [12], SAE [36], ZSL-LP [19], ZSC-SA [20], DASE [21], VSC [37], VSOP [38], f-CLSWGAN [14], CYCLEWGAN [16], and RBGN [39]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Word2 vec**} & \multirow{2}{*}{**Glove**} & \multirow{2}{*}{**Fasttext**} & \multirow{2}{*}{**Bert**} & \multicolumn{3}{c}{**UC Merced Land**} & \multicolumn{1}{c}{**Use Dataset**} \\ \cline{5-8}  & & & & **acc** & \(\mathbf{u}\) & \(\mathbf{s}\) & \(\mathbf{H}\) \\ \hline ✓ & & & & **0.6266** & **0.4869** & 0.7348 & **0.5857** \\  & ✓ & & & 0.5602 & 0.4303 & **0.7389** & 0.5439 \\  & & ✓ & & 0.5358 & 0.4007 & 0.6875 & 0.5063 \\  & & ✓ & & 0.4536 & 0.3507 & 0.7138 & 0.4704 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The experimental results of different word vectors. Bold font denotes the best result. Figure 3: The classification accuracy of different word vectors on the UCM21 dataset. SSE [10] fuses multiple semantic information into the same semantic space and calculates the similarity of them. DMaP [12] studies the relationship between the semantic space manifold and the transferability of visual-semantic mapping. SAE [36] is based on learning a Semantic autoencoder for solving the label prediction problem. SAE adds a constraint from visual mapping to semantic features to alleviate the domain drift problem. ZSL-LP [19] constructs a semantic-directed graph, then uses a label-propagation algorithm for zero-shot classification. ZSC-SA [20] is a novel zero-shot RSSC method, which relies on Sammon embedding and spectral clustering. VSC [37] employs a novel visual architecture constraint for ZSL. VSOP [38] proposes to match latent visual and semantic representations in a shared subspace. DASE [21] proposes a distance-constrained semantic autoencoder to handle ZSRSSC. RBGN [39] employs an adversarial attack and bidirectional generation into GZSL to improve the generalizability and robustness of the model. For a fair comparison with prior methods, we divided the dataset into four seen/unseen ratios, and each ratio was randomly divided 25 times, and 25 zero-shot classification experiments were performed. The average accuracy of the categories is taken as the result. Regarding the splitting ratio setting of the remote sensing dataset, our method is consistent with previous zero-shot classification methods for remote sensing images, such as articles DSAE, ZSC-SA, and ZSL-LP. Tables 2-4 show the classification results for UCM21, AID30, and NWPU45 datasets. As shown in Tables 2-4, our method significantly improved the classification accuracy compared with the state-of-the-art(sota) approaches for zero-shot RSSC in most cases. On the UCM21 dataset, our method improved 4.03%, 8.69%, 9.58%, and 5.99% under different seen/unseen ratios (e.g., 16/5, 13/8, 10/11, and 7/14, respectively). On the AID30 dataset, our method improves 2.37%, 2.61%, 1.05%, and 1.78% under different seen/unseen ratios (e.g., 25/5, 20/10, 15/15, and 10/20, respectively). On the NWPU45 dataset, our method improves 0.24% and 1.45% under two seen/unseen ratios (e.g., 25/20 and 20/25, respectively). The standard deviation of our method is smaller than DSAE in most cases, which also proves the superiority of our method. In conclusion, the results show that our method is more adapted for remote sensing images. The results also show that our method works better with a higher ratio of unseen classes and higher accuracy.