MétaCan
Menu
Retour à la cohorte
Enregistrement W3100418084 · doi:10.5194/epsc2020-475

Change detection analyses using simulated and actual ExoMars TGO-CaSSIS images: A case study based on past and present Gasa Crater gully activity

2020· preprint· en· W3100418084 sur OpenAlexaffabout
Vidhya Ganesh Rangarajan, L. L. Tornabene, G. R. Osinski, F. P. Seelos, Susan J. Conway, Manish Patel, N. Thomas, G. Cremonese, M. Pajola, Giovanni Munaretto, Alice Lucchetti, the CaSSIS Team

Notice bibliographique

Revuenon disponible
Typepreprint
Langueen
DomainePhysics and Astronomy
ThématiquePlanetary Science and Exploration
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésOrbiterImpact craterMars Exploration ProgramGeologyContext (archaeology)MartianVNIRMartian surfaceAstrobiologyRemote sensingHyperspectral imagingBiologyPaleontologyPhysics

Résumé

récupéré en direct d'OpenAlex

Introduction: The Martian surface hosts a variety of active surface processes [1-3] whose regular monitoring is key to providing us insights into past and present-day surface, geologic and climatic conditions [4]. Most change detection studies on Mars utilize time-series image acquisitions from the Mars Reconnaissance Orbiter (MRO) High Resolution Imaging Science Experiment (HiRISE; 25-50 cm/px) [5] and the Context Camera (CTX; 5-6 m/px) [6]. However, the relatively narrow HiRISE colour swath (~20% of the image swath) results in a lower probability of observing surface changes with multiple wavelengths. The Colour and Stereo Surface Imaging System (CaSSIS) onboard the ExoMars Trace Gas Orbiter (TGO) [7] permits 4-band VNIR colour coverage at 4.6 m/px and an image swath >6 km. Furthermore, TGO/CaSSIS is able to observe Mars at multiple times of day, permitting detection/monitoring of diurnal processes. While TGO has only been in operation for a short period of time (MY34-35), the development of simulated CaSSIS images from MRO datasets [8] permits the monitoring of long-term surface changes with CaSSIS from as early as MY28 to present. This work assesses the change detection capabilities of CaSSIS by using a combination of simulated and actual CaSSIS images of one of the most active Martian gully sites to date – Gasa Crater [9-12] . Methods: We initially restricted the simulated CaSSIS images for this study to pre-2012 acquisitions, as our ability to fully photometrically correct CRISM targeted observations for along-track variations in emission/phase is confounded due to the loss of the full gimbal range of CRISM in late 2012 [8]. Three coordinated CRISM/CTX pairs were selected for production into simulated CaSSIS cubes based on a combination of favourable geometries, coverage, estimated atmospheric dust opacities and notable changes. Simulated CaSSIS images were generated using the procedures in [8] where spectrally and spatially resampled CaSSIS-compatible CRISM and CTX products are combined into a rigorous fully-simulated CaSSIS image using a Gram-Schmidt spectral pan-sharpening algorithm, which retains I/F information and minimises colour/spectral distortions [13]. To reduce atmospheric contributions, a dark-object-subtraction technique [14] was applied to both simulated and actual images. All images, including the first actual CaSSIS image acquired on Ls 350, MY34 were overlain and compared to one another to identify visible colour and/or morphologic changes. Notable changes were then compared with previously documented activity. Results and Discussion: We observe 28 possible changes between the simulated and actual CaSSIS image cubes spanning MY28 to MY34 (Fig. 1). Of these, 20 are previously undocumented changes, including 8 putative new changes and 12 fading flows (black-arrows in Fig. 1). All new/previously unrecognized changes are currently under active investigation with associated HiRISE coverage to verify if observed physical changes are not a manifestation of variable illumination conditions. Prominent changes previously observed between MY28 to 30 by [9,10] are all on the northern and north-eastern crater walls (red-arrows in Figs. 1a-c). While one prominent physical change (orange-arrow in Fig. 1c) was previously identified with a simulated CaSSIS image by [8], we note that 5 meter-scale physical changes noted by [9,10] are unresolved by CaSSIS products. Putative new colour changes are observed between MY29 and MY34 (Figs. 1, 2). Six of these were not readily visible with HiRISE due to lack of colour coverage. One prominent example includes a bright-bluish deposit in the eastern part of the crater (Figs. 1b-c). These deposits have an NIR-signature that suggests they are possible ferrous-bearing materials sourced from the gully alcoves [8,12,15]. The most-notable recent putative change based on our first CaSSIS acquisition of Gasa (Ls 350, MY34) shows a bright-bluish deposit on the eastern crater wall that spills partially onto the crater floor (Figs. 1d, 2). Although previous HiRISE acquisitions between MY31 and MY34 seem to show possible morphological changes, lack of HiRISE colour coverage over this deposit makes it difficult to verify the activity. A new optimally-positioned HiRISE acquisition later this year will enable us to verify this surface change (if it has not since faded). Conclusions: This study demonstrates how both simulated and actual CaSSIS cubes are useful for detecting both previously documented and potentially new gully activity at Gasa Crater. While CaSSIS may not capture all small meter-scale physical changes that HiRISE does, it allows for a much-improved colour-change capability over HiRISE. However, continuing to monitor with both instruments is pivotal, as CaSSIS detections of prominent colour changes can be used to reposition HiRISE to better target colour-coverage to validate and characterise meter-scale surface changes. Despite anticipated photometric complications that post-2012 CRISM targeted observations offer, future work will also include an assessment of simulated CaSSIS products generated with post-2012 CRISM and CTX coordinated data to assess identification of both new and previously documented HiRISE changes [16] after MY30. References: [1] McEwen et al. (2010), Icarus 205(1) [2] Bridges et al. (2013), Aeol. Res. 9 [3] Munaretto et al. (2020), PSS 187 [4] Greeley and Iversen (1985), Cambridge Univ. Press [5] McEwen et al. (2007), JGR 112 [6] Malin et al. (2007), JGR 112 [7] Thomas et al. (2017), SSR 212 [8] Tornabene et al. (2018), SSR 214(18) [9] Dundas et al. (2010), GRL 37 [10] Dundas et al. (2012), Icarus 220 [11] Dundas et al. (2015), Icarus 251 [12] Harrison et al. (2019), Geol. Soc. 467(1) [13] Laben et al. (2000), US Patent No: 6011875 [14] Chavez (1988), RSE 24(3) [15] Okubo et al. (2011), Icarus 211(1) [16] Dundas et al. (2019), Geol. Soc. 467(1). Acknowledgements: We thank the spacecraft and instrument engineering teams for the successful completion of the instrument. CaSSIS is a project of the University of Bern and funded through the Swiss Space Office via ESA's PRODEX programme. The instrument hardware development was also supported by the Italian Space Agency (ASI), INAF/Astronomical Observatory of Padova, and the Space Research Center (CBK) in Warsaw. Support from SGF (Budapest), the University of Arizona (Lunar and Planetary Lab.) and NASA are also gratefully acknowledged. The lead author acknowledges support from Tornabene’s funding from the CSA’s Planetary and Astronomy Missions Co-Investigator programme (19PACOI07) and the Canadian NSERC Discovery Grant programme (06418-2020 RGPIN).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,039
Score d'incertitude au seuil0,077

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0000,001
Communication savante0,0010,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,182
Tête enseignante GPT0,353
Écart entre enseignants0,171 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission2
Résumé présentoui

Explorer davantage

Même sujetPlanetary Science and ExplorationTravaux en français237 207