A Color Consistency Processing Method for HY-1C Images of Antarctica

The HY-1C satellite, as part of China’s optical satellite constellation for global ocean monitoring, monitors the ocean and coastal environment by the three broad visible bands of the Coastal Zone Imager (CZI) instrument. However, as a result of the sensor instrument noise, the atmospheric environme...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Zhijiang Li, Haonan Zhu, Chunxia Zhou, Liqin Cao, Yanfei Zhong, Tao Zeng, Jianqiang Liu
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2020
Subjects:
Q
Online Access:https://doi.org/10.3390/rs12071143
https://doaj.org/article/cb1f6bf997b44d00a11e6f32031894d7
Description
Summary:The HY-1C satellite, as part of China’s optical satellite constellation for global ocean monitoring, monitors the ocean and coastal environment by the three broad visible bands of the Coastal Zone Imager (CZI) instrument. However, as a result of the sensor instrument noise, the atmospheric environment during imaging, and the shooting angle, the satellite images often show uneven illumination and inconsistent color between neighboring images. In this paper, according to the characteristics of the HY-1C CZI instrument, we propose a color consistency processing framework for coastal zone images of Antarctica. First of all, the high-frequency and low-frequency information of the image is separated by a statistical filter with simple clustering. The uneven lighting is then replaced by artificial lighting, which is globally uniform. Finally, the color difference between images is corrected by a color transfer method. In order to evaluate the color consistency results quantitatively, a new quantitative evaluation method is proposed. The experimental results for the coastal zone images of Antarctica show that the new processing framework can effectively eliminate the unevenness in the lighting and color. The mosaic results show a good performance in consistent lighting and tones, and the lack of visible mosaic lines proves the effectiveness of the proposed method. The quantitative evaluation analysis confirms the superiority of the proposed method over the Wallis method.