# A Color Consistency Processing Method for HY-1C Images of Antarctica
## 3 Consistency Processing Method
### Color Consistency Framework
#### 3.1.1 Single-view Consistency Process  
Remote sensing images with uneven lighting can be represented by the following mathematical model [23]:  
\[f(x,y)=r(x,y)+\delta(x,y), \tag{4}\]  
where \(f(x,y)\) is the original image, \(\delta(x,y)\) is the uneven lighting information, and \(r(x,y)\) is the ideal image with a uniform brightness distribution. This theory is the basis for the single-view color consistency processing. The most important issue is how to separate the uneven lighting information correctly from the original image. The theoretical basis for achieving color consistency of a single-view image is to extract the uneven lighting information by a reasonable method [24; 25] and then remove it from the original image. Inspired by the mask-based homogenization method [6], we improved the filter and made it suitable for the processing of the HY-1C imagery of Antarctica. We also developed a new single-view image consistency processing algorithm. There were four parts to the single-view consistency processing: (1) feature map extraction; (2) uneven light removal; (3) calculation of the artificial lighting information; and (4) information overlay. The flowchart of the method is shown in Figure 5.  
Figure 4: The processing of HY-1C images of Antarctica in the color consistency framework. CZI, Coastal Zone Imager. Figure 5: The process of single-view color consistency processing. (**a**) The process flow. (**b**) The data flow according to the process flow. In the process flow, there were two important parts: (1) the Gaussian filter with an auxiliary mask; and (2) the artificial lighting information. The filter was used to extract the uneven lighting information from the original image. The traditional Gaussian filter would mix the boundary parts and result in the edge fusion problem (as shown in Figure 6c). In order to optimize the processing of boundaries, a Gaussian filter with an auxiliary mask is proposed to separate the uneven lighting information (feature map) from the image. In addition, we also propose an artificial lighting information calculation algorithm for each image by means of a statistical method. * Feature map extraction  
After Gaussian filtering, the low-frequency information with lighting and color information could be extracted. However, the result of the standard Gaussian filter for the HY-1C images of Antarctica still showed distinct Antarctic ice shelf and ocean parts, which formed high-contrast edges, as shown in Figure 6b. The reason for this was that the filter size for extracting the features was large, and the extracted light information was abnormal in the edge sections. It was clear that the color of the ice shelf boundary in Figure 6c was abnormal, as it was brighter than the inner part. To address this problem, we improved the filter and designed a Gaussian filter with an auxiliary mask. Firstly, the image was clustered with manual adjustment according to the statistical brightness value, to obtain the auxiliary mask. The clustering results could define the Antarctic continent and the other parts, and the auxiliary mask \(mask_{auxiliary}\) could be expressed as follows:  
\[mask_{auxiliary}= \begin{cases}0&(x,y)\
otin Antarctic\,Continent\\ 1&(x,y)\in Antarctic\,Continent\end{cases} . \tag{5}\]  
The auxiliary mask of a sample image is shown in Figure 6d.