A Bayesian Approach to Subkilometer Crater Shape Analysis Using Individual HiRISE Images

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1 Citation (Scopus)

Abstract

The ages of terrains on other planetary bodies are chiefly determined using crater size-frequency distributions. However, primary impacts can generate numerous secondary craters that can affect the crater population. Classifying impact craters as primary or secondary is commonly done via time-consuming manual inspection, which limits the areas that can be analyzed at high resolution. We present a parametric model for characterizing small (100-600 m diameter) impact craters, where the model parameters have implications for describing the physical processes involved in their formation and modification. We infer these parameters from craters in images captured by the high-resolution imaging science experiment (HiRISE) camera onboard the Mars Reconnaissance Orbiter. For each crater within the appropriate size range, our algorithm creates a 3-D surface for a parametrically modeled crater and a 2-D rendering using illumination metadata, including emission, phase, and solar incidence angles at the time when the image was captured. A function describes the likelihood of each set of model parameters in terms of the geometry of craters in a given HiRISE image. These values are then optimized using a Metropolis-Hasting Markov chain Monte Carlo sampler. We evaluated three different prior probability distributions over the parameter space and two different likelihoods: one for digital terrain models and the other for images. We show that after applying t-distributed stochastic neighbor embedding (t-SNE) over the inferred crater parameters, t-SNE is able to project the multidimensional crater parameters into a 2-D space where secondary craters cluster together and are separable from primary craters.

Original languageEnglish (US)
JournalIEEE Transactions on Geoscience and Remote Sensing
DOIs
StateAccepted/In press - May 11 2018

Fingerprint

shape analysis
crater
Imaging techniques
experiment
Experiments
Metadata
Markov processes
Probability distributions
Lighting
Inspection
Cameras
Geometry
science
metadata
digital terrain model
Markov chain
range size
sampler
parameter
Mars

Keywords

  • Image analysis
  • image generation
  • image shape analysis
  • Mars
  • Morphology
  • rendering (computer graphics).
  • Shape
  • Sociology
  • Statistics
  • Surface morphology
  • Surface treatment

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

Cite this

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title = "A Bayesian Approach to Subkilometer Crater Shape Analysis Using Individual HiRISE Images",
abstract = "The ages of terrains on other planetary bodies are chiefly determined using crater size-frequency distributions. However, primary impacts can generate numerous secondary craters that can affect the crater population. Classifying impact craters as primary or secondary is commonly done via time-consuming manual inspection, which limits the areas that can be analyzed at high resolution. We present a parametric model for characterizing small (100-600 m diameter) impact craters, where the model parameters have implications for describing the physical processes involved in their formation and modification. We infer these parameters from craters in images captured by the high-resolution imaging science experiment (HiRISE) camera onboard the Mars Reconnaissance Orbiter. For each crater within the appropriate size range, our algorithm creates a 3-D surface for a parametrically modeled crater and a 2-D rendering using illumination metadata, including emission, phase, and solar incidence angles at the time when the image was captured. A function describes the likelihood of each set of model parameters in terms of the geometry of craters in a given HiRISE image. These values are then optimized using a Metropolis-Hasting Markov chain Monte Carlo sampler. We evaluated three different prior probability distributions over the parameter space and two different likelihoods: one for digital terrain models and the other for images. We show that after applying t-distributed stochastic neighbor embedding (t-SNE) over the inferred crater parameters, t-SNE is able to project the multidimensional crater parameters into a 2-D space where secondary craters cluster together and are separable from primary craters.",
keywords = "Image analysis, image generation, image shape analysis, Mars, Morphology, rendering (computer graphics)., Shape, Sociology, Statistics, Surface morphology, Surface treatment",
author = "Rodrigo Savage and Palafox, {Leon F.} and Morrison, {Clayton T} and Rodriguez, {Jeffrey J} and Barnard, {Jacobus J} and Shane Byrne and Hamilton, {Christopher W}",
year = "2018",
month = "5",
day = "11",
doi = "10.1109/TGRS.2018.2825608",
language = "English (US)",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

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T1 - A Bayesian Approach to Subkilometer Crater Shape Analysis Using Individual HiRISE Images

AU - Savage, Rodrigo

AU - Palafox, Leon F.

AU - Morrison, Clayton T

AU - Rodriguez, Jeffrey J

AU - Barnard, Jacobus J

AU - Byrne, Shane

AU - Hamilton, Christopher W

PY - 2018/5/11

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N2 - The ages of terrains on other planetary bodies are chiefly determined using crater size-frequency distributions. However, primary impacts can generate numerous secondary craters that can affect the crater population. Classifying impact craters as primary or secondary is commonly done via time-consuming manual inspection, which limits the areas that can be analyzed at high resolution. We present a parametric model for characterizing small (100-600 m diameter) impact craters, where the model parameters have implications for describing the physical processes involved in their formation and modification. We infer these parameters from craters in images captured by the high-resolution imaging science experiment (HiRISE) camera onboard the Mars Reconnaissance Orbiter. For each crater within the appropriate size range, our algorithm creates a 3-D surface for a parametrically modeled crater and a 2-D rendering using illumination metadata, including emission, phase, and solar incidence angles at the time when the image was captured. A function describes the likelihood of each set of model parameters in terms of the geometry of craters in a given HiRISE image. These values are then optimized using a Metropolis-Hasting Markov chain Monte Carlo sampler. We evaluated three different prior probability distributions over the parameter space and two different likelihoods: one for digital terrain models and the other for images. We show that after applying t-distributed stochastic neighbor embedding (t-SNE) over the inferred crater parameters, t-SNE is able to project the multidimensional crater parameters into a 2-D space where secondary craters cluster together and are separable from primary craters.

AB - The ages of terrains on other planetary bodies are chiefly determined using crater size-frequency distributions. However, primary impacts can generate numerous secondary craters that can affect the crater population. Classifying impact craters as primary or secondary is commonly done via time-consuming manual inspection, which limits the areas that can be analyzed at high resolution. We present a parametric model for characterizing small (100-600 m diameter) impact craters, where the model parameters have implications for describing the physical processes involved in their formation and modification. We infer these parameters from craters in images captured by the high-resolution imaging science experiment (HiRISE) camera onboard the Mars Reconnaissance Orbiter. For each crater within the appropriate size range, our algorithm creates a 3-D surface for a parametrically modeled crater and a 2-D rendering using illumination metadata, including emission, phase, and solar incidence angles at the time when the image was captured. A function describes the likelihood of each set of model parameters in terms of the geometry of craters in a given HiRISE image. These values are then optimized using a Metropolis-Hasting Markov chain Monte Carlo sampler. We evaluated three different prior probability distributions over the parameter space and two different likelihoods: one for digital terrain models and the other for images. We show that after applying t-distributed stochastic neighbor embedding (t-SNE) over the inferred crater parameters, t-SNE is able to project the multidimensional crater parameters into a 2-D space where secondary craters cluster together and are separable from primary craters.

KW - Image analysis

KW - image generation

KW - image shape analysis

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KW - Morphology

KW - rendering (computer graphics).

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KW - Sociology

KW - Statistics

KW - Surface morphology

KW - Surface treatment

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