A new cloud removal algorithm for multi-spectral images

Abstract

Multi-spectral images acquired by different image sensors from satellites or aircrafts are often covered with cloud under bad weather condition. In this paper, we propose a new cloud removal method to restore the cloud-covered area of multi-spectral images using only a couple of multi-spectral images taken of the same scene. In our algorithm, we take the registered visual and infrared images as an example. We first de-noise the two images with the method of Wiener Filter to wipe off the primary noise. As the infrared imaging has more powerful ability of penetrating through cloud than the visual image, we adopt the method of Poisson Matting to exactly segment the edge of area covered by infrared cloud and use wavelet analyzing to restore the area originally occupied by infrared cloud. Then a B-spline based model is hired to repair the residual holes. For the corresponding visual image of the same scene by taking the spacial correlation between the two multi-spectral images, the location of targets under visual cloud is reconstructed and their texture styles are finally recovered by colorization method. Our algorithm is easy to manipulate and can also be extended to other multi-spectral wavebands.