Haze removal using deep convolutional neural network for Korea Multi-Purpose Satellite-3A (KOMPSAT-3A) multispectral remote sensing imagery

This paper presents a convolutional neural network to automatically remove the haze distribution using a single multispectral remote sensing image in the raw file format. To train the proposed dehazing network, we synthesized multispectral hazy images using the haze thickness map (HTM) and relative scattering model representing the wavelength-dependent scattering property of the haze distribution. Since the raw multispectral hazy images have a low dynamic range, we cannot accurately estimate the haze distribution directly from them. To differently impose a proper amount of attention to hazy and haze-free regions, we used the HTM from the contrast-enhanced version of the input hazy image. The proposed dehazing network consists of four sub-networks: (i) shallow feature extraction network (SFEN), (ii) cascaded residual dense block network (CRDBN), (iii) multiscale feature extraction network (MFEN), and (iv) refinement network (RN). The densely connected convolutional layers and local residual learning allow the residual dense block (RDB) to extract the abundant local features, and the cascaded architecture further improves the propagation of the local information and gradients. The MFEN is used to extract multiscale local features representing the hierarchical information for the haze distribution and haze-free region. Experimental results demonstrated that the proposed method can achieve improved dehazing performance on Korea Multi-Purpose Satellite-3A (KOMPSAT-3A) multispectral remote sensing imagery without undesired artifacts. In the sense of quantitative assessment, the proposed method produced improved peak signal-to-noise ratio (PSNR) by 10%, structural similarity index measure (SSIM) by 1%, and spectral angle mapper (SAM) by 19% compared with the existing best method.