@inproceedings{f1920a9861a9461bab1f55288403e095,
title = "Stray light correction method for VIIRS DNB based on automatic region division",
abstract = "Stray light contamination is an important factor affecting the nighttime remote sensing image quality, especially affecting the quantitative application of low-light-level remote sensing data. VIIRS Day/Night Band(DNB) data is the most widely used nighttime low-light-level data in the world. However, the stray light contamination impacts the DNB sensor's nighttime scenes. In this paper, we analyzed the characteristics of VIIRS DNB stray light and designed a simple and effective stray light correction method based on automatic region division. We preprocessed the data that meet certain solar zenith angle(SZA) requirements to obtain the true stray light data. An automatic region division method according to the SZA is designed to find the specific area affected by stray light and get the boundary of different region. Polynomial curve fitting based on least square method was used to get the mathematical model of the stray light. Then the mathematical model was used to do the stray light correction. We use the DNB northern hemisphere Earth view data to verify this method. Experimental result shows that the proposed method can effectively remove stray light and restore the surface information while preserving the radiation information.",
keywords = "Day/Night Band (DNB), VIIRS, automatic region division, low-light-level, stray light correction",
author = "Yuqing He and Yu Gao and Xin Guo and Junyuan Zhao",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; 2022 Applied Optics and Photonics China: Optical Sensing, Imaging, and Display Technology, AOPC 2022 ; Conference date: 18-12-2022 Through 19-12-2022",
year = "2023",
doi = "10.1117/12.2652075",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Yadong Jiang and Xiaoyong Wang and Yongtian Wang and Dong Liu and Bin Xue",
booktitle = "AOPC 2022",
address = "United States",
}