A spatial-spectral adaptive thin-cloud removal method based on slow feature analysis

Abstract

ABSTRACT Clouds cover corrupts the spatial and spectral information of optical remote sensing (RS) images, which seriously affects the use of RS data. To solve the problem of missing information, a spatial-spectral adaptive method based on slow feature analysis (SFA) is proposed to restore cloudy scenes in this letter. SFA converts the sequence signal into slowly varying signal signatures and the clouds will be located in the first component. We propose spatial and spectral adaptive correction methods to reduce interference from highlighted pixels and jointly constrain the cloud coefficients in each band according to reflectance and gradient. The effectiveness of our method is verified on Landsat-8 OLI simulated and real cloudy datas, the restored results are visually rich in texture detail and moderately corrected. The average PSNR of the four real scenes is 42.9738 dB and coefficient of determination (R 2) is 0.8203, and many indicators have proved that our method is better than the existing methods.