Assessing the Spectral Capability of the HiRISE Colour Channels: A Re-Visit to the CRISM Type-Locality Sites on Mars at Higher Resolution
Bibliographic record
Abstract
The High Resolution Imaging Science Experiment (HiRISE) onboard the Mars Reconnaissance orbiter has been acquiring images of the Martian surface at scales of ~25-50cm/px since 2006 [1]. The dataset has been instrumental in helping understand a variety of past and present geologic processes (e.g., [2,3]), and support landing site safety certification and science exploration for future missions (e.g., [4,5]). Apart from high-resolution panchromatic information provided by 10 overlapping CCDs with a broadband RED filter (~690nm), HiRISE also acquires colour infrared information along a central strip with two additional filters (BG: ~500 nm and IR: ~870 nm). While this colour swath is narrow and limited in coverage (and has recently become narrower still), it has provided crucial information for characterizing several colour-associated surface changes (e.g., [6,7]).Most compositional information of Mars is provided at relatively medium-to-coarse resolution (10s to 100s of m/px) by Observatoire pour la Minéralogie, l’Eau, les Glaces et l’Activité (OMEGA) and the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM). With the loss of CRISM’s L-detector in 2018, and subsequent decommissioning of the instrument in 2022 [8], it is important for us to understand the spectral capabilities of presently operational higher-resolution multispectral instruments in orbit like HiRISE and the Colour and Stereo Surface Imaging System (CaSSIS; ~4m/px). Recent studies (e.g., [7]) have demonstrated that HiRISE products available through the PDS, which are subject to cosmetic clipping of the values at extremes of the image histogram, may not be ideal for quantitative spectral analysis. Alternatively, generation of unfiltered data products, free from such cosmetic modifications, have been shown to be beneficial for spectral characterization of surface materials like pure water-ice [7,9].We attempt to further explore this capability, to assess the spectral sensitivity of the three HiRISE colour channels to help characterise a variety of surface minerals that have been identified on Mars. We have been acquiring dedicated HiRISE colour observations at all CRISM mineral/phase type-locality sites identified by [10]. In this work, we describe how the three HiRISE colour wavelengths resolve each mineral/phase, and the extent to which HiRISE colour may be able to discriminate between them. We also provide band ratios and spectral parameters that are useful for mitigating the effects of the variable atmospheric opacity and illumination. Altogether, HiRISE colour products will be useful for future surface characterization studies, and permit co-analysis with other operational multispectral datasets like CaSSIS [11] and the High Resolution Stereo Camera (HRSC) [12].References:[1] McEwen et al., (2007), JGR, E05S02, https://doi.org/10.1029/2005JE002605[2] Dundas et al., (2021), JGR 126(8), https://doi.org/10.1029/2021JE006876[3] Daubar et al., (2022), JGR 127, https://doi.org/10.1029/2021JE007145[4] Mandon et al., (2021), Astrobiology 21(4), https://doi.org/10.1089/ast.2020.2292[5] Fawdon et al., (2024), Journal of Maps 20(1), https://doi.org/10.1080/17445647.2024.2302361[6] Dundas et al. (2023), GRL 50(2), https://doi.org/10.1029/2022GL100747[7] Rangarajan et al. (2024a), Icarus 419, https://doi.org/10.1016/j.icarus.2023.115849[8] Seelos et al. (2024), Icarus 419, https://doi.org/10.1016/j.icarus.2023.115612[9] Rangarajan et al. (2024b), 10th Mars Conf., https://www.hou.usra.edu/meetings/tenthmars2024/pdf/3224.pdf[10] Viviano et al. (2014), JGR 119(6), https://doi.org/10.1002/2014JE004627[11] Tornabene et al. (2024), EPSC2024-1231, https://doi.org/10.5194/epsc2024-1321[12] Jaumann et al. (2007), PSS 55, https://doi.org/10.1016/j.pss.2006.12.003
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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) | 0.000 | 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) | 0.002 | 0.001 |
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".