# Multi-Input Attention Network for Dehazing of Remote Sensing Images
## 1 Introduction
In remote sensing images, the distribution of haze is often uneven, and thin clouds often exist, which makes the image more disturbed than the images studied in their research. Second, remote sensing images usually have more than three channels (RGB) as in ordinary natural images. For example, the images obtained by the Operational Land Imager (OLI) of landsat8 have nine bands. The images acquired by the Multispectral Imager (MSI) of Sentinel-2 have 13 bands. The bands beyond RGB also interfere with haze. Figure 1 shows an example of an image captured by Sentinel-2. It can be observed that the infrared bands, such as Band11 and Band12, have stronger penetrating power and are less impacted by haze. Most of the previous dehazing methods cannot effectively remove non-uniformly distributed haze. They cannot deal with the impact on the bands beyond RGB as well. Inspired by the dehazing of natural images using convolutional neural networks (CNNs), some researchers apply CNNs to the dehazing of remote sensing images [21; 22; 23; 24]. Meanwhile, the infrared bands and Synthetic Aperture Radar (SAR) microwave bands in multispectral remote sensing images can penetrate haze more easily compared to visible bands. They can better reserve the ground information in the area with haze. Therefore, some research uses infrared band images and SAR images as auxiliary information, which is used as input for CNNs to obtain dehazing models [25; 26; 27]. These methods can better handle the non-uniform distribution of haze. However, most of them focus on the RGB band or a few near-infrared bands, instead of the more abundant infrared bands. Furthermore, most of the training data are synthetic hazy images, which could be different from the actual hazy images with a lot more complexity. To address these issues, we propose a multi-input attention network for the dehazing of a single multispectral remote sensing image. Since different bands in multispectral images have different features, we utilize the strong penetration capability of richer infrared bands and divide the multiple bands into three groups to extract features. Our proposed network consists of an encoder-decoder structure and uses head-to-tail connections and a multi-scale output structure, similar to the feature pyramid network. This structure enables the dehazing model to effectively remove haze while maintaining ground details. It can directly process bands of different resolutions. Furthermore, improved channel attention and spatial attention structure are added for extracting features from different inputs, which improves the efficiency and adaptability of training. In this research, we use real haze and haze-free multispectral remote sensing image pairs as datasets. Our dehazing model achieves very good results in restructuring a variety of cloud-contaminated multispectral images. The main contributions of this study are as follows:  
* We propose a multi-input attention network to dehaze multispectral remote sensing images. This method does not require upsampling/downsampling on the training data. It can dehaze the images captured by Sentinel-2 with different resolutions in nine bands, effectively avoiding information loss due to upsampling/downsampling. To obtain the best recovery effect, the visible light band and the features of the infrared band are fused. It takes the advantage of the strong penetration capability of the infrared band. * We build an end-to-end dehazing network with an encoder-decoder structure, which directly obtains haze-free images from learning hazy images. To improve training efficiency, the structure of weighted multiplication and residual connection between different input lines are used to adjust the feature extraction. * We use skip connections and a multi-layer output structure in the network, which can produce multi-spectral dehazing images with different resolutions. Connecting the shallow part with the tail of the network preserves the ground details and allows the network to fully extract deep features.