An auto-adapting global-to-local color balancing method for optical imagery mosaic

摘要

This paper presents a novel auto-adapting global-to-local color balancing method which aims to eliminate the effects of color differences between adjacent optical images to achieve seamless image mosaicking. The proposed method combines global and local optimization strategies to eliminate color differences between different target images adaptively without assigning the reference image. The global optimization strategy takes the constraint that the color information of the image before and after the color balancing process should be minimal, by which the assigning of reference images can be avoided. The strategy takes all target images as a whole and solves the normalization regression models simultaneously, which transfers the color difference elimination problem into the least square optimization one and eliminates the total color differences effectively. The local optimization strategy is a supplement for the global one, which focuses on the local information to eliminate the color differences in the overlap areas of the target images with the Gamma transform algorithm. It is worth noting that the proposed method can select a suitable processing flow from both the global and local optimization aspects based on the characteristics of the target images. When the total overlap rate of the target images is small, both the global and local strategies are employed; and when the total overlap rate of the target images is large, only the local optimization strategy is employed, by which a seamless color balancing result can be generated. The experimental results in this paper demonstrate that the proposed method performs well in color balancing for multi-type optical datasets. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.