In-flight radiometric calibration of the ExoMars TGO Colour and Stereo Surface Imaging System
Bibliographic record
Abstract
The Colour and Stereo Surface Science Imaging System (CaSSIS) of the ExoMars Trace Gas Orbiter returns on average twenty images per day of the Martian surface, most of them in 3 or 4 colours and some of them in stereo. CaSSIS uses a push-frame approach to acquire colour images, with four bandpass filters deposited directly above the sensor and an imaging cadence synchronized with the ground track velocity to cover the imaged area with tens of small, partially overlapping images. These “framelets” are later map-projected and mosaicked to build the final image. This approach offers both advantages and challenges in terms of radiometric calibration. While the collection of dark and flatfield frames is considerably enhanced by the frequent and fast acquisition of tens of successive images, mosaics assembled from the adjacent framelets highlight the straylight and changes in the bias of the detector. Both issues have been identified on CaSSIS images, with low intensities overall (up to a few %), but sufficient to generate prominent artefacts on the final assembled colour images. We have therefore developed methods to correct these artefacts that are now included into the radiometric calibration pipeline. We detail here the different steps of the calibration procedure and the generation of the products used for calibration, and discuss the efficacy of the corrections. The relative uncertainties on the bias and flatfield frames are low, of the order of 0.2 and 0.1%, respectively. The uncertainty on the absolute radiometric calibration is of 3%, which is quite low for such an instrument. The straylight adds an estimated ∼1% error to the absolute calibration. The residuals after corrections of the straylight and bias offsets are of the order of a few DNs to tens of DNs. As CaSSIS can observe the Martian surface in challenging illumination conditions to provide unique views of the surface at early and late local solar time, residuals from the straylight correction can become noticeable when the absolute signal is very low. As they appear at the level of the noise in very low illumination images, these residuals do not limit the scientific exploitation of the data. For most of the dataset, as the signal in well-exposed images reaches 8000 DNs in the panchromatic filter and thousands of DNs in the colour filters, the residuals are negligible and CaSSIS provides the best colour images available over many areas covered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".