Description
Color balancing algorithms such as histogram matching are often applied to remote sensing data to make images of the same area taken at different times appear visually consistent with a reference image. However, if the input data differs from the reference image due to clouds, snow, or other issues, existing color balancing methods can produce severe artifacts. We introduce a new method, implemented using the Scipy stack, that fits a smooth color transfer function based on coregistered points and a-priori knowledge of the approximate white balance for the image. This approach provides color consistency with the reference image without introducing visually unrealistic artifacts when clouds and snow are present.