# Multi-Input Attention Network for Dehazing of Remote Sensing Images
## 5 Conclusions  
Traditional dehazing methods rely on prior features and are less versatile, which makes them not applicable, especially for remote sensing images with widespread non-uniform haze. In recent years, deep learning methods have been applied for automatic feature extraction. However, the structure of remote sensing images with haze is relatively complex. It is difficult for general neural networks to effectively extract features. Meanwhile, there are very few network models targeting muti-band remote sensing images. In this research, we propose a multi-input, multi-spectral remote sensing image dehazing network, which effectively utilizes the penetrating capability of the infrared band to haze. We used global skip connections and attention modules to achieve effective feature extraction and maintain ground details in the meantime. Finally, we designed experiments to test the performance of the proposed method on multispectral images captured by Sentinel-2, which have different degrees of haze effects. Our method can effectively restore the images. It outperforms the traditional dark channel method and several neural network methods, such as DehazeNet, AOD-Net, MSBDN and GridDehazeNet, in terms of haze residues and quantitative evaluation metrics.  
Meanwhile, there are some limits in this research. First, the training dataset is not categorized based on the types of haze, which could impact the effectiveness of the proposed model. Second, even though the ground details are well maintained in the restored images, there is still some loss compared to haze-free images. As for our future work, we will formulate an indicator to describe the degree of the haze effect, which will be used to classify the images in the training dataset. At the same time, we will improve the model by referring to the method that can effectively improve the detail resolution in super-resolution research.  
Conceptualization, Z.H.; Data curation, Z.H.; Software, Z.H. and F.Z.; Writing--original draft preparation, Z.H.; Supervision, C.G.; Methodology, Y.H.; Resources, Y.H.; Validation, F.Z. and L.L.; Writing--review and editing, C.G. and L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant No. 31970378), Science and Technology Commission of Shanghai, Shanghai 2021 \"Science and Technology Innovation Action Plan\" social development science and technology research project (Grant No. 21 DZ1202500). Shanghai Water Authority Science and Technology Project (Grant No. 2021-10). Jiangsu provincial water resources department, Jiangsu Province Water Conservancy Science and Technology Project (Grant No. 2020068).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Components**} \\ \cline{2-7}  & **Multi-Input** & **SA** & **CA** & **PSNR** & **SSIM** & **FSIM** \\ \hline Baseline & & & & 24.573 & 0.725 & 0.818 \\ \hline Model-1 & & ✓ & & 24.861 & 0.733 & 0.806 \\ \hline Model-2 & & & ✓ & 25.061 & 0.744 & 0.832 \\ \hline Model-3 & & ✓ & ✓ & 25.224 & 0.761 & 0.840 \\ \hline Model-4 & ✓ & & & 26.641 & 0.809 & 0.858 \\ \hline Model-5 & ✓ & ✓ & & 27.021 & 0.831 & 0.889 \\ \hline Model-6 & ✓ & & ✓ & 26.891 & 0.813 & 0.894 \\ \hline Proposed & ✓ & ✓ & ✓ & 27.866 & 0.858 & 0.908 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experimental results of different structural models.