High-accuracy source-independent radiometric calibration with low complexity for infrared photonic sensors

Guo, Qiang; Chen, Fuchun; Li, Xiangyang; Chen, Boyang; Wang, Xin; Chen, Guilin; Wei, Caiying

doi:10.1038/s41377-021-00597-4

Cited by 8 publications

(3 citation statements)

References 43 publications

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“…The above-mentioned constrains cumulatively reduce the measurement precision and overall consistency of data acquired from global in-orbit infrared sensors [12]. RC without employing radiometric sources, in comparison, is historically less investigated and applied [28].…”

Section: Introductionmentioning

confidence: 99%

Towards Artificial Intelligence Based Radiometric Calibration for In-orbit Remote Sensing Satellites

Chen,

Wu,

Hui

et al. 2024

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

Towards Artificial Intelligence Based Radiometric Calibration for In-orbit Remote Sensing Satellites

Chen,

Wu,

Hui

et al. 2024

Preprint

Self Cite

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“…Accurate infrared radiometric calibration technology provides the basis for accurate inversion of target radiation intensity, which is crucial for target classification and identification in military contexts. Although the sensor is radiometrically calibrated before a flight, the detector-response relationship changes after orbit due to environmental changes, time drift, and so on [2]. Therefore, high-precision radiometric calibration in orbit is essential.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Infrared Stellar Flux Based on Star Catalogs with I-GWO for Stellar Calibration

Hong,

Rao,

Zhou

et al. 2024

Remote Sensing

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As on-orbit space cameras evolve toward larger apertures, wider fields of view, and deeper cryogenic environments, achieving absolute radiometric calibration using an all-optical path blackbody reference source in orbit becomes increasingly challenging. Consequently, stars have emerged as a novel in-orbit standard source. However, due to differences in camera bands, directly obtaining the stellar radiance flux corresponding to specific camera bands is not feasible. In order to address this challenge, we propose a method for estimating radiance flux based on the MSX star catalog, which integrates a dual-band thermometry method with an improved grey wolf optimization (I-GWO) algorithm. In an experiment, we analyzed 351 stars with temperatures ranging from 4000 to 7000 K. The results indicate that our method achieved a temperature estimation accuracy of less than 10% for 83.5% of the stars, with an average estimation error of 5.82%. Compared with previous methods based on star catalogs, our approach significantly enhanced the estimation accuracy by 75.4%, improved algorithm stability by 91.3%, and reduced the computation time to only 3% of that required by other methods. Moreover, the on-orbit star calibration error using our stellar radiance flux estimation method remained within 5%. This study effectively leveraged the extensive data available in star catalogs, providing substantial support for the development of an infrared star calibration network, which holds significant value for the in-orbit calibration of large-aperture cameras. Future research will explore the potential applicability of this method across different spectral bands.

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“…Therefore, the quadratic NL coefficient is calculated in the laboratory calibration before launch and adopted directly for utilization under the in-orbit condition for most infrared sensors. Theoretically, however, both the linearity and the NL terms are affected by the background radiation changes from the environmental components (Guo et al, 2021a), the thermal fields of which consist of different working conditions of a sensor (i.e. GIIRS), so the NL coefficient is inconstant with respect to the linearity response.…”

mentioning

confidence: 99%

A New Non-linearity Correction Method for Spectrum from GIIRS onboard Fengyun-4 Satellites and its Preliminary Assessments

Guo,

Liu,

Wang

et al. 2024

Preprint

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Abstract. Non-linearity (NL) correction is a critical procedure to guarantee the calibration accuracy of a spaceborne sensor to approach a good level (i.e. better than 0.5 K). Unfortunately, such a NL correction is still unemployed in spectrum calibration of Geostationary Interferometric InfraRed Sounder (GIIRS) onboard Fengyun-4A (FY-4A) satellite. Different from the classical NL correction method where the NL coefficient is estimated from out-band spectral artifacts in an empirical low-frequency region originally with prelaunch results and updated under in-orbit condition, a new NL correction method for a spaceborne Fourier transform spectrometer (including GIIRS) is proposed. Particularly, the NL parameter μ independent of different working conditions (namely the thermal fields from environmental components) can be achieved from laboratory results before launch and directly utilized for in-orbit calibration. Moreover, to overcome the inaccurate linear coefficient from two-point calibration influencing the NL correction, an iteration algorithm is established to make both the linear and the NL coefficients to be converged to their stable values with the relative errors less than 0.5 % and 1 % respectively, which is universally suitable for NL correction of both infrared and microwave sensors. By using the onboard internal blackbody (BB) which is identical with the in-orbit calibration, the final calibration accuracy for both all the detectors and all the channels with the proposed NL correction method is validated to be around 0.2–0.3 K at an ordinary reference temperature of 305 K. Significantly, in the classical method, the relative error of NL parameter immediately transmitting to that of linear one in theory which will introduce some additional errors around 0.1–0.2 K for the interfered radiance inevitably, no longer exists. Moreover, the adopted internal BB with the higher emissivity will produce the better NL correction performance in practice. The proposed NL correction method is scheduled for implementation to GIIRS onboard FY-4A satellite and its successor after modifying their possible spectral response function variations.

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High-accuracy source-independent radiometric calibration with low complexity for infrared photonic sensors

Cited by 8 publications

References 43 publications

Towards Artificial Intelligence Based Radiometric Calibration for In-orbit Remote Sensing Satellites

Towards Artificial Intelligence Based Radiometric Calibration for In-orbit Remote Sensing Satellites

Estimation of Infrared Stellar Flux Based on Star Catalogs with I-GWO for Stellar Calibration

A New Non-linearity Correction Method for Spectrum from GIIRS onboard Fengyun-4 Satellites and its Preliminary Assessments

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