Feature Extraction and Classification from Planetary Science Datasets enabled by Machine Learning

In this paper we present two examples of recent investigations that we have undertaken, applying Machine Learning (ML) neural networks (NN) to image datasets from outer planet missions to achieve feature recognition. Our first investigation was to recognize ice blocks (also known as rafts, plates, polygons) in the chaos regions of fractured ice on Europa. We used a transfer learning approach, adding and training new layers to an industry-standard Mask R-CNN (Region-based Convolutional Neural Network) to recognize labeled blocks in a training dataset. Subsequently, the updated model was tested against a new dataset, achieving 68% precision. In a different application, we applied the Mask R-CNN to recognize clouds on Titan, again through updated training followed by testing against new data, with a precision of 95% over 369 images. We evaluate the relative successes of our techniques and suggest how training and recognition could be further improved. The new approaches we have used for planetary datasets can further be applied to similar recognition tasks on other planets, including Earth. For imagery of outer planets in particular, the technique holds the possibility of greatly reducing the volume of returned data, via onboard identification of the most interesting image subsets, or by returning only differential data (images where changes have occurred) greatly enhancing the information content of the final data stream

Cassini Titan flyby

<p>List of Cassini's flyby on Titan extracted from NASA/JPL Cassini RADAR Users Guide.</p

PovRay model of fractal aerosols

This script render a synthetic image of fractal aerosols.Fractal aggregateBased on Botet et al. (1995) we build a collection of fractal aggregates of 1, 2, 4, 8, 16, 32, 64, 128, 256, 512 and 1024 monomers. Their reduce coordinates are stored in a fractals.db SQLite database and can be dumped like this:sqlite3 fractals.db 'SELECT x,y,z FROM Geo_128 WHERE k=7' -header -columnThen this database is used by a python script to generate a PovRay scene:python fractals-draw.py 128

Modeling the radar response of Linear Dunes and Mega-Yardangs on Titan: Geological implications

<p>Linear geomorphology features on Earth are well-studied using geo- logical field observations and remote sensing (optical, radar). These structures can be divided into two groups : the wind-deposit and ac- cumulation areas known as linear sand dunes ; and the wind-abraded ridges on eroded cohesive rocks known as mega-yardangs.</p> <p>In this study, we performed a comparative planetology approach to discriminate between these two features on Saturn's moon Titan, by comparing the radar signature of their terrestrial analogues. The Ku- band (13.8 GHz) Synthetic Aperture Radar (SAR) onboard the Cassini spacecraft is able to sense through the optically-opaque atmosphere of Titan, but the resolution of this instrument (200 m) is not sufficient to clearly observe the morphology of linear structures. We then considered orbital radar images of terrestrial structures, provided by the high reso- lution (18 m) X-band radar (9.6 GHz) of the TerraSAR-X mission. We acquired images of the Great Sand Sea (Egypt) and the Namib Desert (Namibia) for the linear dunes ; and of the Lut Desert (Iran) and the Dju- rab Desert (Chad) for the mega-yardangs. We modeled the geometrical effect of the topography using DEM models provided by SRTM and GDEM, and the surface roughness effect was estimated using single scattering models such as Integral Equation model and Geometrical Optics model. We were able to reproduce in a quite faithful way the backscattered radar signal by terrestrial dunes and mega-yardangs.</p> <p>These results were applied on Titan to the analysis of T8 and T49 fly- bys over the Bellet equatorial region, which contains linear dune fields, and to the analysis of T64 and T83 flybys, which show potential mega- yardang structures. Based on our quantitative radar modeling results, we can confirm that linear structures in T64 and T83 are actually mega- yardangs. As on Earth mega-yardang are usually formed over former lakes depressions on homogeneous rocks with a strong and stable uni- directional wind erosion over million years. Their existence on Titan could indicate that lakes were existing in lower latitude regions in the past, providing a new constrain for past climatic models of Titan.</p

Detection of sediment transport in Kraken Mare with a radiative transfert model using an aerosol vertical profile and optical properties adapted to Titan North pole

International audienc

Detection of sediment transport in Kraken Mare with a radiative transfert model using an aerosol vertical profile and optical properties adapted to Titan North pole

International audienc

Transparency of the 2 μm (5000 cm-1) methane window in Titan's atmosphere and impact on retrieved surface reflectivity

International audienceThe study of Titan properties with remote sensing relies on a good knowledge of the atmosphere properties. The in-situ observations made by Huygens combined with recent advances in the definition of methane properties enable to model and interpret observations with a very good accuracy. Thanks to these progresses, we can analyze in this work the observations made at the limb of Titan in order to retrieve information on the haze properties as its vertical profiles and its spectral behaviour along the VIMS/Cassini range (from 0.88 to 5.1 μm). However, for applications to real atmospheres, one need to account for the widening of the spectroscopic lines (e.g., Voigt profile) and apply an empirical cut-off of the far wings. In general, this is a multiplying function of the wavenumber, f(ν), applied to the Voigt profile that allows a faster decay of the wing profile beyond a given distance from the center of the line ν0 : f(ν)=1 if |ν- ν0| ≤ Δν, and f(ν)=exp(-|ν- ν0|/ σ) if |ν- ν0| 〉 Δν. Although the 2-μm window is apparently straitforward to model, it appears that the standard cut-off parameters (that is Δν ~ 26 cm-1 and σ ~ 120 cm-1) which is used for other windows in Titan's atmosphere is not adequat for this window. Other sets of parameter must be used to reproduce Titan spectrum at 2 μm. However, there is no convergence of the results between these works and a large variety of cut-off parameters are used. Alternatively, it was found that some gas absorptions (ethane and another unknown gas) leave a signature around 2-μm and also affect the transparency in this window. In our study we make an exhaustive investigation on the cut-off parameters to determine which are the best couples of parameters to fit the 2-μm window. We also evaluated how gaseous absorptions can allow to reach a satisfactory agreement and, especially, if it allows to match observations with the standard cut-off. Finally, we investigate the impact of the different solutions (different cut-off, with or without supplementary absorptions) on the retrieved surface albedo

Haze seasonal variations of Titan's upper atmosphere

This study presents a 13 years survey of haze UV extinction profiles, monitoring the temporal evolution of the detached haze layer (DHL) in Titan's upper atmosphere (350-600 km). As reported by West et al. 2011 (GRL vol.38, L06204) at the equator, we show that the DHL is present at all latitudes below 55°N during the northern winter (2004-2009). Then, it globally sunk and disappeared in 2012. No permanent DHL was observed between 2012 and 2015. It's only in late-2015, that a new structure emerged from the Northern hemisphere and propagated to the equator. This new DHL is not as pronounced as in 2004 and is much more complex than the one observed earlier. In one specific sequence, in 2005, we were able to investigate the short time scale variability of the DHL and no major changes was observed. When both side of the limb were visible (dawn/dusk), we notice that the extinction of the DHL is slightly higher on the dawn side. Additionally, during a polar flyby in 2009, we observed the longitudinal variability of the DHL and spotted some local inhomogeneities. Finally, comparisons with UVIS stellar occultations and General Climate Models (GCMs) are both consistent with our findings. However, we noticed that the timing of the DHL main pattern predicted by the GMCs can be off by up to 30° in solar longitude. All these observations bring new perspectives on the seasonal cycle of Titan's upper atmosphere, the evolution of the DHL and its interaction with the dynamics.This dataset contains the list of all the ISS images and UV extinction profiles derived from your analysis, plus the source code to reproduce the figures.JPL-URS290356-CL#21-0547Related Publication: Haze seasonal variations of Titan's upper atmosphere during the Cassini Mission Benoît Seignovert Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA Rannou Pascal GSMA, Université de Reims Champagne-Ardenne, UMR 7331-GSMA, 51687 Reims, France West Robert A. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA Vinatier Sandrine LESIA, Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Université de Paris, 5 place Jules Janssen, 92195 Meudon, France The Astrophysical Jounal (ApJ) 2021-01-25 https://doi.org/10.3847/1538-4357/abcd3b en