  Haze removal in aerial images is a challenging problem due to considerable
variation in spatial details and varying contrast. Changes in particulate
matter density often lead to degradation in visibility. Therefore, several
approaches utilize multi-spectral data as auxiliary information for haze
removal. In this paper, we propose SkyGAN for haze removal in aerial images.
SkyGAN consists of 1) a domain-aware hazy-to-hyperspectral (H2H) module, and 2)
a conditional GAN (cGAN) based multi-cue image-to-image translation module
(I2I) for dehazing. The proposed H2H module reconstructs several visual bands
from RGB images in an unsupervised manner, which overcomes the lack of hazy
hyperspectral aerial image datasets. The module utilizes task supervision and
domain adaptation in order to create a "hyperspectral catalyst" for image
dehazing. The I2I module uses the hyperspectral catalyst along with a
12-channel multi-cue input and performs effective image dehazing by utilizing
the entire visual spectrum. In addition, this work introduces a new dataset,
called Hazy Aerial-Image (HAI) dataset, that contains more than 65,000 pairs of
hazy and ground truth aerial images with realistic, non-homogeneous haze of
varying density. The performance of SkyGAN is evaluated on the recent
SateHaze1k dataset as well as the HAI dataset. We also present a comprehensive
evaluation of HAI dataset with a representative set of state-of-the-art
techniques in terms of PSNR and SSIM.
