Optimal Illumination and Color Consistency for Optical Remote-Sensing Image Mosaicking

Illumination and color consistency are very important for optical remote-sensing image mosaicking. In this letter, we propose a simple but effective technique that simultaneously performs image illumination and color correction for multiview images. In this framework, we first present an uneven illumination removal algorithm based on bright channel prior, which guarantees the illumination consistency inside a single image. We then adapt a pairwise color-correction method to coarsely align the color tone between source and reference images. In this stage, we give a new single-image quality metric which combines brightness deviation, color cast, and entropy together for automatic reference-image selection. Finally, we perform a least-squares adjustment (LSA) procedure to obtain optimal illumination and color consistency among multiview images. In detail, we first perform a pairwise image matching by using SIFT algorithm; once sparse local patch correspondences obtained, the illumination and color relationship between images can be established based on a global gamma correction model; the illumination and color errors can then be minimized by LSA. Extensive experiments on both challenging synthetic and real optical remote-sensing image data sets show that it significantly outperforms the compared state-of-the-art approaches. All the source code and data sets used in this letter are made public.