Multi-Input Attention Network for Dehazing of Remote Sensing Images

Abstract

The non-uniform haze distribution in remote sensing images, together with the complexity of the ground information, brings many difficulties to the dehazing of remote sensing images. In this paper, we propose a multi-input convolutional neural network based on an encoder–decoder structure to effectively restore remote sensing hazy images. The proposed network can directly learn the mapping between hazy images and the corresponding haze-free images. It also effectively utilizes the strong haze penetration characteristic of the Infrared band. Our proposed network also includes the attention module and the global skip connection structure, which enables the network to effectively learn the haze-relevant features and better preserve the ground information. We build a dataset for training and testing our proposed method. The dataset consists of remote sensing images with two different resolutions and nine bands, which are captured by Sentinel-2. The experimental results demonstrate that our method outperforms traditional dehazing methods and other deep learning methods in terms of the final dehazing effect, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and feature similarity (FSIM).