Radiometric Calibration Using Artificial Intelligence: Constituting Uniform Observing Systems for Infrared Satellites

作者：Boyang Chen, Aiqun Wu, Hui Wen, Peng Rao, Xuan Feng, Fansheng Chen, Changpei Han, Qichao Ying, Yapeng Wu, Miao Liu, D. G. Moss, Zhenxing Qian · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2025 · DOI：10.1109/tgrs.2025.3534794 · 被引用次数：4 · 研究领域：Calibration and Measurement Techniques

Radiometric calibration (RC) is a critical process in aerospace infrared remote sensing that establishes the relationship between the radiation energy of observed objects and the digital number (DN) output from sensors, which is fundamental for ensuring high-precision applications of infrared remote sensing data. At present, source-based RC (SBRC) is the predominant method, relying on a variety of radiometric sources (RSs) including in-orbit blackbodies, or natural targets such as lakes and oceans. This approach, while effective, imposes constraints on remote sensing systems such as space & weight allocation for RS and additional observation time for RC. Moreover, the reliance on physical calibration sources can introduce uncertainties due to factors such as imperfect emissivity of in-orbit blackbodies, lack of data consistency due to varied RS types, and variations in environmental conditions. In this article, we propose a novel RC method named artificial intelligence RC (AIRC), which directly generates RC coefficients for the in-orbit remote sensing satellites using the physical and environmental parameters of the sensor. We first theoretically prove that RC coefficients can be derived as functions of the sensor states. Next, we propose our neural networks for infrared RC (RCNN), to learn this relationship based on historical high-accuracy calibration data, enabling a shift from reference traceability (RT) to states traceability (ST). Then, to verify the feasibility of the ...