Pluto's Surface Mapping using Unsupervised Learning from Near-Infrared Observations of LEISA/Ralph

• URL: http://arxiv.org/abs/2301.06027v1
• Date: Sun, 15 Jan 2023 07:15:52 GMT
• Title: Pluto's Surface Mapping using Unsupervised Learning from Near-Infrared Observations of LEISA/Ralph
• Authors: A. Emran, C. M. Dalle Ore, C. J. Ahrens, M. K. H. Khan, V. F. Chevrier, and D. P. Cruikshank
• Abstract summary: We map the surface of Pluto using an unsupervised machine learning technique using the near-infrared observations of the LEISA/Ralph instrument onboard NASA's New Horizons spacecraft. Average I/F spectra of each unit were analyzed -- in terms of the position and strengths of absorption bands of abundant volatiles such as N$_2$, CH$_4$, and CO and nonvolatile H$_2$O -- to connect the unit to surface composition, geology, and geographic location. The distribution of surface units shows a latitudinal pattern with distinct surface compositions of volatiles
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We map the surface of Pluto using an unsupervised machine learning technique using the near-infrared observations of the LEISA/Ralph instrument onboard NASA's New Horizons spacecraft. The principal component reduced Gaussian mixture model was implemented to investigate the geographic distribution of the surface units across the dwarf planet. We also present the likelihood of each surface unit at the image pixel level. Average I/F spectra of each unit were analyzed -- in terms of the position and strengths of absorption bands of abundant volatiles such as N${}_{2}$, CH${}_{4}$, and CO and nonvolatile H${}_{2}$O -- to connect the unit to surface composition, geology, and geographic location. The distribution of surface units shows a latitudinal pattern with distinct surface compositions of volatiles -- consistent with the existing literature. However, previous mapping efforts were based primarily on compositional analysis using spectral indices (indicators) or implementation of complex radiative transfer models, which need (prior) expert knowledge, label data, or optical constants of representative endmembers. We prove that an application of unsupervised learning in this instance renders a satisfactory result in mapping the spatial distribution of ice compositions without any prior information or label data. Thus, such an application is specifically advantageous for a planetary surface mapping when label data are poorly constrained or completely unknown, because an understanding of surface material distribution is vital for volatile transport modeling at the planetary scale. We emphasize that the unsupervised learning used in this study has wide applicability and can be expanded to other planetary bodies of the Solar System for mapping surface material distribution.

Related papers

• Investigating the Capabilities of Deep Learning for Processing and Interpreting One-Shot Multi-offset GPR Data: A Numerical Case Study for Lunar and Martian Environments [9.150932930653921]
  Ground-penetrating radar (GPR) is a mature geophysical method that has gained increasing popularity in planetary science over the past decade. GPR has been utilised both for Lunar and Martian missions providing pivotal information regarding the near surface geology of Terrestrial planets. This paper investigates the potential of deep learning for interpreting and processing GPR data.
  arXiv  Detail & Related papers  (2024-10-18T11:38:29Z)
• A Machine Learning Approach to Detecting Albedo Anomalies on the Lunar Surface [0.0]
  This study introduces a data-driven approach using machine learning (ML) techniques to explore and predict albedo anomalies on the Moon's surface. The primary objective is to identify relationships between chemical elements and albedo, thereby expanding our understanding of planetary surfaces. We present an interactive analytical tool to visualize prediction errors, delineating their spatial and chemical characteristics.
  arXiv  Detail & Related papers  (2024-07-08T11:25:30Z)
• Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection [1.8434042562191815]
  We advocate the application of machine learning (ML) techniques for anomaly (novelty) detection to exoplanet transit spectra. We demonstrate the feasibility of two popular anomaly detection methods on a large public database of synthetic spectra.
  arXiv  Detail & Related papers  (2023-08-15T07:19:54Z)
• Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data [1.8111829286068908]
  A pipeline for unsupervised mapping of spectra on a mine face is proposed. The proposed pipeline brings together unsupervised and self-supervised algorithms in a unified system to map minerals on a mine face. The consistency of its mapping capability is demonstrated using data acquired at two different times of day.
  arXiv  Detail & Related papers  (2023-02-09T21:03:03Z)
• Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission [61.26321023273399]
  A significant point-like component from the small scale (or discrete) structure in the H2 interstellar gas might be present in the Fermi-LAT data. We show that deep learning may be effectively employed to model the gamma-ray emission traced by these rare H2 proxies within statistical significance in data-rich regions.
  arXiv  Detail & Related papers  (2022-06-06T18:00:07Z)
• Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra [68.8204255655161]
  We focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We uncover interesting structures in the principal component basis, namely, well-defined branches corresponding to different chemical regimes.
  arXiv  Detail & Related papers  (2022-01-07T22:26:33Z)
• Unsupervised Spectral Unmixing For Telluric Correction Using A Neural Network Autoencoder [58.720142291102135]
  We present a neural network autoencoder approach for extracting a telluric transmission spectrum from a large set of high-precision observed solar spectra from the HARPS-N radial velocity spectrograph.
  arXiv  Detail & Related papers  (2021-11-17T12:54:48Z)
• Spectral-Spatial Global Graph Reasoning for Hyperspectral Image Classification [50.899576891296235]
  Convolutional neural networks have been widely applied to hyperspectral image classification. Recent methods attempt to address this issue by performing graph convolutions on spatial topologies.
  arXiv  Detail & Related papers  (2021-06-26T06:24:51Z)
• Surface Warping Incorporating Machine Learning Assisted Domain Likelihood Estimation: A New Paradigm in Mine Geology Modelling and Automation [68.8204255655161]
  A Bayesian warping technique has been proposed to reshape modeled surfaces based on geochemical and spatial constraints imposed by newly acquired blasthole data. This paper focuses on incorporating machine learning in this warping framework to make the likelihood generalizable. Its foundation is laid by a Bayesian computation in which the geological domain likelihood given the chemistry, p(g|c) plays a similar role to p(y(c)|g.
  arXiv  Detail & Related papers  (2021-02-15T10:37:52Z)
• Spatial-Spectral Manifold Embedding of Hyperspectral Data [43.479889860715275]
  We propose a novel hyperspectral embedding approach by simultaneously considering spatial and spectral information. spatial-spectral manifold embedding (SSME) models the spatial and spectral information jointly in a patch-based fashion. SSME not only learns the spectral embedding by using the adjacency matrix obtained by similarity measurement between spectral signatures, but also models the spatial neighbours of a target pixel in hyperspectral scene.
  arXiv  Detail & Related papers  (2020-07-17T05:40:27Z)
• Interpreting Galaxy Deblender GAN from the Discriminator's Perspective [50.12901802952574]
  This research focuses on behaviors of one of the network's major components, the Discriminator, which plays a vital role but is often overlooked. We demonstrate that our method clearly reveals attention areas of the Discriminator when differentiating generated galaxy images from ground truth images.
  arXiv  Detail & Related papers  (2020-01-17T04:05:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.