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Record W4408484790 · doi:10.5194/egusphere-egu25-18947

Assessing the Spectral Capability of the HiRISE Colour Channels: A Re-Visit to the CRISM Type-Locality Sites on Mars at Higher Resolution

2025· preprint· en· W4408484790 on OpenAlexaff
Vidhya Ganesh Rangarajan, L. L. Tornabene, Ernst Hauber, Solmaz Adeli, Patrick Russell, J. J. Wray, A. Delamere, F. P. Seelos

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsWestern University
Fundersnot available
KeywordsMars Exploration ProgramLocalityRemote sensingGeologyHigh resolutionResolution (logic)AstrobiologyComputer scienceGeographyArtificial intelligencePhysicsLinguistics

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.052
GPT teacher head0.309
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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