# A Color Consistency Processing Method for HY-1C Images of Antarctica
## 1 Introduction  
The HY-1C satellite is the third experimental satellite launched by China for monitoring the ocean surface. The HY-1C and HY-1D satellites launched in 2018 and 2019, respectively, undertake the observation of the global ocean surface in the morning and the afternoon. The Coastal Zone Imager (CZI) instrument onboard the HY-1C satellite has the characteristics of a large swath width and a high spatial resolution, and it produces observations with a high contrast. It can capture real-time observations of coastal areas and can obtain the distribution of suspended sediments in estuaries and harbors. In addition, marine environmental concerns such as sea ice, red tide, green tide, and pollution events can be monitored by the CZI instrument [1; 2; 3]. Due to the parameters of the imaging instrument, light source, and shooting angle, the lighting in HY-1C imagery is not uniform, and the color between the images is inconsistent. As a result,during image mosaicking, a significant blocky effect occurs in the mosaic result, which interferes with target recognition and other observations. Therefore, color consistency processing, as a mosaic preprocessing task, is an important consideration. The traditional methods for the color consistency processing of remote sensing images are mostly based on image processing tools or adjustment functions, according to the images to be corrected [4; 5]. However, it is difficult to define the processing rules for different images. In addition, the manual operation takes much time and effort. Inspired by the digital image homogenization algorithms, some automated machine processing methods for digital images have been applied to the color consistency processing of remote sensing images. For example, [PERSON] et al. [6] presented a processing flow and a corresponding processing approach based on the mask dodging principle. This method uses a mathematical model to simulate the changes of brightness and compensate the different parts of the image. It can achieve a satisfactory effect and can remove the uneven illumination of optical images. However, how to set the filter size is an open question. Based on the mask principle, a number of methods using specific filters have been proposed, such as the improved mask dodging method [7], improved mask methods [8; 9; 10; 11], and the Wallis filter [12]. These methods are all based on filtering in the spatial domain. However, limited by the texture, color, and filter, these methods all need to be adjusted to the specific image characteristics when utilized in practice. To address this issue, [PERSON] et al. [13] proposed a uniform algorithm based on adaptive templates, which works by fitting the secondary surface of the brightness mean of the local area. However, this approach fails for complex brightness distributions and large-sized images, due to the number of windows and the amount of calculation. In addition to filtering in the spatial domain, some approaches have been developed for filtering in the frequency domain [14; 15; 16]. For example, a method for removing the uneven light from images using multiple wavelet transformations was proposed by [PERSON] et al. [17]. This method takes into account both the frequency domain and the spatial domain of the digital images and improves the efficiency of the algorithm and the quality of the results. In addition, the homomorphic filtering method [18] utilizes a filter function to highlight the high frequency and weaken the low frequency, which helps the low-contrast images achieve improved brightness consistency. The principle of physiology is also used in digital image processing. For example, [PERSON] et al. [19] presented an approach based on physical transformation and psychological perception. Based on the principle of the visual physiology of the human eye, retinex homogenization algorithms [20; 21; 22] have been used to realize consistent processing of images by means of retinal mechanism simulation.