1. Introduction
Remote Sensing (RS) imagery data provides an excellent opportunity in Earth Observation (EO) to analyze and obtain information to understand the Earth's resources and physical phenomena parameters. However, clouds cover more than half of the Earth, according to statistics. This is a common issue with optical RS images causing information to be obscured by clouds and their associated shadows. As a result, we cannot capture reliable information from corrupted images unless we use clear sky images for the same time that they are not available. Therefore, one reasonable solution is improving the networks by leveraging trustworthy and transparent physical properties. Our proposed method is exploiting spectral angular distance (SAD) to train cycle-consistent adversarial networks with illumination invariant features that is illustrated in Figure 1.