# A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica
## 1 Introduction
Color consistency processing is used to solve the problem of the color difference among multiple images, and general methods include the histogram matching method, mathematical model method, and mean variance method. A histogram matching method adjusts the histogram of the image according to a specified shape so that it has an approximate shape with the target image and achieves the goal of color consistency. [PERSON] et al. [28] used the histogram features of overlapping image regions to establish mapping relationships and then processed the image gray level to match the color of the target image. [PERSON] et al. [29] optimized the mapping rules during histogram matching, which enriched the image gray level. [PERSON] et al. [30] proposed a guided initial solution of the histogram extreme point matching algorithm to address the global consistency optimization. A mathematical model method mainly statisticizes the pixel features of overlapping areas between images and then fits a linear [31; 32] or nonlinear [33; 34; 35] relationship between the image to be corrected and the target image; thus, image color consistency correction is carried out. [PERSON] et al. [2] proposed a pairwise gamma model to coarsely align the color tone between the source and reference images. Then the multiview color alignment is minimized by the least squares adjustment method. [PERSON] et al. [36] proposed a series of local linear model approaches and a specific global cost function that can correct global and local color discrepancies simultaneously and preserve image gradient as much as possible. [PERSON] et al. [37] proposed pseudo-invariant feature-based algorithms with polygon features through single-band and multiple-band regression, and applied them to radiometric normalization between Sentinel-2A and Landsat-8 OLI images. The effectiveness of the proposed algorithm is demonstrated by comparing the results with the histogram matching method. A mean variance method, such as the Wallis transform, converts the mean value and variance of an image to be processed into the mean value and variance of the target image through a mathematical relationship to achieve color matching. Based on Wallis, [PERSON] et al. [38] proposed a color balance method that considers both the whole and part of the image, which reduces the dependence of color balance processing on the effect of uniform light processing. [PERSON] [39] studied the problem of grayscale loss after Wallis transformation and verified the effectiveness of the color consistency method. [PERSON] [20] proposed a block weighted Wallis image color consistency processing method to solve the color and contrast differences between images. Different from the method that fits the pixel-to-pixel features of overlapping region images as previously mentioned, histogram matching is a distribution-based color consistency matching method [19; 40]. It can avoid subjectivity in the selection of pseudo point features and image misregistration [37]. The focus of this algorithm is on the digital number (DN) distribution between the subject image and the target image, which is closely linked to the type of terrain objects on the image. Therefore, when using histogram matching methods for color correction, it is necessary to consider the distribution of surface-truth objects on the image. Antarctica is surrounded by ocean and covered by ice and snow year-round. It has a large contrast and dynamic range in optical satellite images. The simplicity of terrain objects on the Antarctic continent is a great guarantee for the applicability of the algorithm. However, if there is a large difference between the types of terrain objects in the subject and reference images, for example, if the subject image includes seawater and glaciers, while the reference image only contains glaciers, the histogram matching results are often poor.