# Vision-Language Models for Zero-Shot Classification of Remote Sensing Images
## 4 Experimental Results
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & & & & & & & \\ \hline \multicolumn{2}{c}{**Model**} & \multicolumn{2}{c}{**Prompt 1**} & \multicolumn{2}{c}{**Prompt 2**} & \multicolumn{2}{c}{**Prompt 3**} & \multicolumn{2}{c}{**Prompt 4**} & \multicolumn{2}{c}{**Prompt 5**} & \multicolumn{2}{c}{**Prompt 6**} & \multicolumn{2}{c}{**Average**} \\ \hline \multirow{5}{*}{Clip} & RN50-quickgelu & 42 & 43 & 46 & 47 & 46 & 48 & 45.33 \\ \cline{2-9}  & RN101-quickgelu & 50 & 48 & 54 & 52 & 54 & 54 & 52.00 \\ \cline{2-9}  & RN50x64 & 59 & 59 & 65 & 62 & 69 & 60 & 62.33 \\ \cline{2-9}  & ViT-B-32 & 49 & 50 & 57 & 55 & 54 & 55 & 53.33 \\ \cline{2-9}  & ViT-B-16 & 54 & 55 & 64 & 59 & 61 & 63 & 59.33 \\ \cline{2-9}  & ViT-L-14 & 59 & 56 & 63 & 61 & 63 & 61 & 60.50 \\ \hline \multirow{5}{*}{Open-Clip} & convnext\_base\_w & 55 & 58 & 61 & 59 & 61 & 61 & 59.17 \\ \cline{2-9}  & convnext\_large\_d & 55 & 61 & 61 & 59 & 66 & 61 & 60.50 \\ \cline{2-9}  & ViT-L-14\({}^{\prime}\) & 51 & 56 & 59 & 58 & 60 & 55 & 56.50 \\ \cline{2-9}  & ViT-B-16\({}^{\prime}\) & 49 & 56 & 50 & 54 & 59 & 51 & 53.17 \\ \cline{2-9}  & ViT-B-32\({}^{\prime}\) & 36 & 47 & 46 & 50 & 51 & 45 & 45.83 \\ \cline{2-9}  & ViT-H-14\({}^{\prime}\) & 56 & 59 & 63 & 56 & 67 & 66 & 61.17 \\ \cline{2-9}  & xlm-roberta-large & 66 & 69 & 67 & 69 & 70 & 69 & 68.33 \\ \hline \multicolumn{2}{c}{Average} & 51.25 & 54.00 & 57.42 & 56.00 & 59.25 & 56.67 & \\ \hline \multicolumn{2}{c}{**(C)**} & \multicolumn{2}{c}{**Model**} & \multicolumn{2}{c}{**Prompt 1**} & \multicolumn{2}{c}{**Prompt 2**} & \multicolumn{2}{c}{**Prompt 3**} & \multicolumn{2}{c}{**Prompt 4**} & \multicolumn{2}{c}{**Prompt 5**} & \multicolumn{2}{c}{**Prompt 6**} & \multicolumn{2}{c}{**Average**} \\ \hline \multirow{5}{*}{Clip} & RN50-quickgelu & 33 & 35 & 42 & 43 & 45 & 42 & 40.00 \\ \cline{2-9}  & RN101-quickgelu & 41 & 48 & 54 & 53 & 50 & 53 & 49.83 \\ \cline{2-9}  & RN50x64 & 57 & 57 & 60 & 62 & 64 & 61 & 60.17 \\ \cline{2-9}  & ViT-B-32 & 45 & 46 & 57 & 53 & 51 & 55 & 51.17 \\ \cline{2-9}  & ViT-B-16 & 44 & 51 & 61 & 58 & 58 & 60 & 55.33 \\ \cline{2-9}  & ViT-L-14 & 59 & 59 & 65 & 63 & 64 & 67 & 62.83 \\ \hline \multirow{5}{*}{Open-Clip} & convnext\_base\_w & 55 & 57 & 60 & 59 & 62 & 60 & 58.83 \\ \cline{2-9}  & convnext\_large\_d & 60 & 60 & 65 & 62 & 68 & 64 & 63.17 \\ \cline{2-9}  & ViT-L-14\({}^{\prime}\) & 58 & 59 & 64 & 65 & 66 & 61 & 62.17 \\ \cline{2-9}  & ViT-B-16\({}^{\prime}\) & 57 & 57 & 57 & 59 & 58 & 55 & 57.17 \\ \cline{2-9}  & ViT-B-32\({}^{\prime}\) & 48 & 48 & 48 & 51 & 51 & 47 & 48.83 \\ \cline{2-9}  & ViT-H-14\({}^{\prime}\) & 62 & 60 & 71 & 64 & 72 & 72 & 66.83 \\ \cline{2-9}  & xlm-roberta-large & 59 & 62 & 63 & 63 & 66 & 62 & 62.50 \\ \hline \multicolumn{2}{c}{Average} & 52.15 & 53.77 & 59.00 & 58.08 & 59.62 & 58.38 & \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._Figure 3: Confusion matrix for (**a**) Merced dataset obtained by xlm-roberta-large (OA = 75%) and (**b**) ViT-H-14 (OA = 73%). Figure 4: Confusion matrix for **(a)** AID dataset obtained by xlm-roberta-large (OA = 70%) and **(b)** ViT-H-14 (OA = 73%). The results presented in Table 1 shed light on the significance of employing RS-related terms in the prompts, which consistently yield higher accuracy compared to CLIP's original prompt, \"a photo of a \(<\)class_name\(>\)\". Notably, prompt template 5, \"a satellite image of a \(<\)class_name\(>\)\", emerges as particularly effective, demonstrating slightly superior average accuracy across all three datasets. Figure 5: Confusion matrix for (**a**) NWPU dataset obtained by xlm-roberta-large (OA = 66%) and (**b**) ViT-H-14 (OA = 72%). On a conclusive note, the top-performing models across the three datasets are identified as xlm-roberta-large and ViT-H-14 from Open-CLIP. Specifically, for the Merced dataset, zero-shot overall accuracy (OA) reaches impressive levels of 75% and 73% (xlm-roberta-large and ViT-H-14).