# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 4 Experiments and Discussions
### Application to a High-Level Vision Task  
Dehazing is a crucial image preprocessing step for high-level computer vision tasks. In this regard, we compared the road extraction results of D-LinkNet [47] using dehazed images generated by different dehazing methods, as presented in Figure 6. The first and second rows of Figure 6 are the input and corresponding output images of the D-LinkNet road extraction algorithm, respectively. Figure 6a shows the original hazy image, while Figure 6b-n present the results obtained using different dehazing methods. Figure 6o is provided as the ground truth for reference. As demonstrated in Figure 6a, the D-LinkNet algorithm fails to work effectively due to the interference of haze in the original hazy image. Although DehazeFlow and YOLY have removed some haze, the dehazed results are not sufficient for D-LinkNet to extract any road information, as presented in Figure 6e,l, respectively. The dehazed image generated by ZID has severe color distortion, resulting in D-LinkNet extracting only a few roads. In comparison to other dehazing methods, our proposed method generates more continuous road lines, and the extracted road masks are closer to the reference ground truth. Therefore, our proposed method demonstrates superior performance as an image preprocessing step for D-LinkNet. To quantitatively evaluate road extraction accuracy, we calculated four commonly used evaluation metrics in object detection and segmentation tasks: _Precision_, _Recall_, _IoU_, and _F1-score_. The four metrics are defined as follows:  
\[Precision=\frac{TP}{TP+FP};Recall=\frac{TP}{TP+FN};IoU=\frac{TP}{TP+FN+FP} \tag{16}\]  
\[F1\text{-}score=\frac{2*Precision*Recall}{Precision+Recall} \tag{17}\]  
Here, _TP_, _FP_, and _FN_ denote true positives, false positives, and false negatives, respectively. The _F1-score_ is a weighted harmonic mean of _Precision_ and _Recall_, which \begin{table}
\begin{tabular}{l l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Density**} & \multirow{2}{*}{**Thin**} & \multicolumn{4}{c}{**Moderate**} & \multicolumn{4}{c}{**Thick**} & \multicolumn{4}{c}{**Average**} \\ \cline{3-13}  & & & **Methods** & **PSNR** & **SSIM** & **LPIPS** & **PSNR** & **SSIM** & **LPIPS** & **PSNR** & **SSIM** & **LPIPS** & **PSNR** & **SSIM** & **LPIPS** \\ \hline \multirow{3}{*}{Traditional} & _DCP_ & **17.07** & **0.82** & **0.123** & **16.93** & **0.81** & **0.130** & **15.81** & **0.76** & **0.159** & **16.61** & **0.80** & **0.137** \\  & CEFE & 15.20 & 0.75 & 0.154 & 15.27 & 0.74 & 0.164 & 15.00 & 0.74 & 0.175 & 15.16 & 0.74 & 0.164 \\  & FADE & 15.97 & 0.76 & 0.149 & 49.2 & 0.72 & 0.179 & 14.28 & 0.72 & 0.188 & 15.05 & 0.73 & 0.171 \\ \hline \multirow{3}{*}{Learning-based} & DehazeFlow & 14.09 & 0.77 & 0.136 & 14.44 & 0.79 & 0.132 & 12.73 & 0.68 & 0.216 & 13.75 & 0.75 & 0.162 \\  & SKRTDN & 14.36 & 0.77 & 0.191 & 14.20 & 0.80 & 0.178 & 13.42 & 0.73 & 0.233 & 14.00 & 0.77 & 0.201 \\  & DWGAN & 16.73 & **0.84** & 0.116 & 18.26 & **0.86** & 0.106 & 17.34 & **0.82** & 0.138 & 17.44 & **0.84** & 0.120 \\  & RefineNet & 16.68 & 0.83 & **0.091** & 17.00 & 0.84 & **0.092** & 16.98 & **0.82** & **0.109** & 16.89 & 0.83 & **0.097** \\  & USIDNet & **18.99** & 0.78 & 0.170 & **18.51** & 0.76 & 0.185 & **17.39** & 0.73 & 0.207 & **18.30** & 0.76 & 0.187 \\ \hline \multirow{3}{*}{Zero-shot} & ZID & 11.32 & 0.57 & 0.218 & 12.02 & 0.60 & 0.202 & 12.20 & 0.61 & 0.212 & 11.85 & 0.59 & 0.211 \\  & DODP & 15.56 & 0.79 & 0.108 & 16.41 & 0.81 & 0.099 & 15.91 & 0.79 & 0.122 & 15.96 & 0.80 & 0.110 \\  & YOLY & **18.14** & **0.84** & 0.088 & 17.66 & 0.84 & 0.091 & 15.96 & 0.78 & 0.138 & 17.25 & 0.82 & 0.104 \\ \cline{1-1}  & ZIR & 14.79 & 0.80 & 0.118 & 15.31 & 0.83 & 0.113 & 13.64 & 0.74 & 0.179 & 14.58 & 0.79 & 0.137 \\ \cline{1-1}  & Ours & 17.54 & **0.84** & **0.084** & **17.91** & **0.86** & **0.086** & **16.85** & **0.81** & **0.120** & **17.43** & **0.84** & **0.098** \\ \hline \hline \end{tabular}
\end{table}