Aiqun Wu

Radiometric Calibration (RC) is a key step in high-precision aerospace infrared remote sensing. Currently, source-based RC is the most prevailing methodologies, where a radiometric source such as blackbody with known radiation energy is observed by the sensor and the corresponding output digital number is recorded. However, the methodology is limited by several constraints, e.g., space and weight requirement for in-orbit blackbodies, and lack of uniformity and ideal properties of blackbodies. This paper presents a new artificial intelligence based RC that directly generates RC coefficients given the state of the sensor parameters. First, we derive the relationship between the RC coefficients and the physical parameters and environmental parameters of the sensor. Next, we steadily collect a large quantity of remote sensing data using our in-orbit satellites since 2018 and correspondingly train a neural network to fit such function for the generation of RC coefficients. Finally, our extensive experiments show that the proposed method achieves high-accuracy RC comparable with state-of-the-art methods that use in-orbit blackbody. To the best of our knowledge, we are the first to demonstrate an artificial-intelligence-based RC method for satellites without observation to blackbodies during in-orbit remote sensing. We anticipate that the new approach could promote the accuracy and uniformity of global infrared remote sensing data, thereby promoting the ability to discern natural laws and understand longterm environmental or celestial trends. Besides, it can substantially save the cost for carrying and observing in-orbit blackbodies.