The existence of cloud shadows in remote sensing images severely affects image interpretation and application. Therefore, finding an efficient shadow compensation method is essential to recover the shaded information. Because the calculation method for obtaining γ coefficient in the conventional Gamma transformation compensation model relies only on the statistical characteristics of the shadowed area, this method cannot reflect the difference between the pixels within the shadowed area. Therefore, this study proposes a self-adaptive Gamma algorithm using the image information from different levels to compensate for the shaded information automatically in the cloud shadows. First, the cloud shadows were detected using remote sensing images and the feature mean and variance of the shadow regions were calculated. Then, the improved logarithmic transformation γ-factor calculation method was designed. By synthesizing the multi-level mean variance information of the local window of pixels in the shadow area, the shadow area and non shadow area, the weighted solution can reflect the overall characteristics and internal differences of the shadow area. Moreover, this method can realize the pixel-level adaptive compensation. Therefore, each pixel in the cloud shadow area can be reasonably compensated to achieve the same effect as the nonshaded area. The experimental results show that the proposed method can effectively compensate the cloud shadow cast by clouds with different shapes and thicknesses. The brightness and contrast of cloud shadows are considerably improved. Additionally, the details of the regional features in cloud shadows are better recovered.