# A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica
## 2 Data and Methods  
### Data and Preprocessing  
The Antarctic images studied in the paper were obtained using the CZI on board the Chinese HY-1C and HY-1D satellites. The data cover the near-infrared (0.76-0.89 \(\upmu\)m), red (0.61-0.69 \(\upmu\)m), green (0.52-0.60 \(\upmu\)m), and blue (0.42-0.50 \(\upmu\)m) bands; the spatial resolution is 50 m; and the swath is 900 km. The data product has an L1C processing level, is stored in 32 bit floating-point GeoTIFF format, and is projected in polar stereographic projection over the Antarctic region after radiometric and geometric correction. Four CZI scene datasets were selected (Table 2), and the imaging dates were 3 December 2021, 6 December 2021, 8 December 2021, and 13 December 2021. The data range covers Victoria Land, the Ross Ice Shelf, and the Transantarctic Mountains in Antarctica. There are abundant topographic and geomorphic features in this region, including mountains, glaciers, ice shelves, bare rocks, blue ice, and oceans.  
To reduce the data processing amount for subsequent research, channels 3 (red), 2 (green), and 1 (blue) were selected, and the data type was converted to 8-bit. Then, the data were combined into a true-color RGB image. During satellite acquisition of polar images, because of the sun altitude angle and terrain changes, sometimes, a small amount of abnormally large gray values exist in a few images. To make a better visual effect of the image during the data-type conversion process, a cumulative histogram is introduced into the conversion method to set the extreme value of the image stretch. The maximum intercept frequency is set to \(\sigma_{\text{max}}=(1-0.005)\), and the pixel value \(pix(\sigma_{\text{max}})\) corresponding to frequency \(\sigma_{\text{max}}\) in the cumulative histogram is the maximum stretch value in the entire image. A pixel value larger than \(pix(\sigma_{\text{max}})\) was set to 255 because pixels  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Image Date** & **Filename** \\ \hline
1 & 3 December 2021 & H1D\_OPER\_CZI\_L1C\_20211203T055702, 20211203T055757, 07755,10 \\
2 & 6 December 2021 & H1D\_OPER\_CZI\_L1C\_20211206T091605, 20211206T091700, 07799,10 \\
3 & 8 December 2021 & H1D\_OPER\_CZI\_L1C\_20211208T094805, 20211208T094900, 07827,10 \\
4 & 13 December 2021 & H1D\_OPER\_CZI\_L1C\_20211213T052354, 20211213T052449,07899,10 \\ \hline \end{tabular}
\end{table}
Table 2: HY-1/CZI datasets used in our experiments.  
with an image value of less than \(pix(\sigma_{\max})\) are converted to 0-255 using linear stretching according to the minimum image value and the range between \(pix(\sigma_{\max})\). The conversion procedure is as follows:  
\[f(x,y)_{\text{int}}=\text{int}\big{(}f(x,y)_{\text{float}}\times 100\big{)} \tag{1}\]  
\[p_{max}=pix(\sigma_{\max}) \tag{2}\]  
\[p_{min}=min(f(x,y)_{\text{int}}) \tag{3}\]  
\[f(x,y)=\begin{cases}255&f(x,y)_{\text{int}}>p_{max}\\ 255\times\frac{f(x,y)_{\text{int}}-p_{min}}{p_{max}-p_{min}}&f(x,y)_{\text{int} }\leq p_{max}\end{cases} \tag{4}\]  
where \(f(x,y)_{\text{float}}\) is the L1C product pixel value, \(f(x,y)_{\text{int}}\) is the converted integer data, and \(f(x,y)\) is the converted 8-bit value.