  Multi-spectral satellite imaging sensors acquire various spectral band images
such as red (R), green (G), blue (B), near-infrared (N), etc. Thanks to the
unique spectroscopic property of each spectral band with respective to the
objects on the ground, multi-spectral satellite imagery can be used for various
geological survey applications. Unfortunately, image artifacts from imaging
sensor noises often affect the quality of scenes and have negative impacts on
the applications of satellite imagery. Recently, deep learning approaches have
been extensively explored for the removal of noises in satellite imagery. Most
deep learning denoising methods, however, follow a supervised learning scheme,
which requires matched noisy image and clean image pairs that are difficult to
collect in real situations. In this paper, we propose a novel unsupervised
multispectral denoising method for satellite imagery using wavelet subband
cycle-consistent adversarial network (WavCycleGAN). The proposed method is
based on unsupervised learning scheme using adversarial loss and
cycle-consistency loss to overcome the lack of paired data. Moreover, in
contrast to the standard image domain cycleGAN, we introduce a wavelet subband
domain learning scheme for effective denoising without sacrificing high
frequency components such as edges and detail information. Experimental results
for the removal of vertical stripe and wave noises in satellite imaging sensors
demonstrate that the proposed method effectively removes noises and preserves
important high frequency features of satellite images.
