The Experts below are selected from a list of 135 Experts worldwide ranked by ideXlab platform
Robert T. Pappalardo - One of the best experts on this subject based on the ideXlab platform.
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Surface Sulfur Detection via Remote Sensing and Onboard Classification
ACM Transactions on Intelligent Systems and Technology, 2012Co-Authors: Lukas Mandrake, Kiri L. Wagstaff, Damhnait Gleeson, Umaa Rebbapragada, Daniel Tran, Steve Chien, Robert T. Pappalardo, David R. Thompson, Rebecca CastanoAbstract:Orbital remote sensing provides a powerful way to efficiently survey targets such as the Earth and other planets and moons for features of interest. One such feature of astrobiological relevance is the presence of surface Sulfur Deposits. These Deposits have been observed to be associated with microbial activity at the Borup Fiord glacial springs in Canada, a location that may provide an analogue to other icy environments such as Europa. This article evaluates automated classifiers for detecting Sulfur in remote sensing observations by the hyperion spectrometer on the EO-1 spacecraft. We determined that a data-driven machine learning solution was needed because the Sulfur could not be detected by simply matching observations to Sulfur lab spectra. We also evaluated several methods (manual and automated) for identifying the most relevant attributes (spectral wavelengths) needed for successful Sulfur detection. Our findings include (1) the Borup Fiord Sulfur Deposits were best modeled as containing two sub-populations: Sulfur on ice and Sulfur on rock; (2) as expected, classifiers using Gaussian kernels outperformed those based on linear kernels, and should be adopted when onboard computational constraints permit; and (3) Recursive Feature Elimination selected sensible and effective features for use in the computationally constrained environment onboard EO-1. This study helped guide the selection of algorithm parameters and configuration for the classification system currently operational on EO-1. Finally, we discuss implications for a similar onboard classification system for a future Europa orbiter.
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Biosignature Detection at an Arctic Analog to Europa
Astrobiology, 2012Co-Authors: Damhnait Gleeson, Robert T. Pappalardo, Mark S. Anderson, Stephen E. Grasby, Randall E. Mielke, Katherine E. Wright, Alexis S. TempletonAbstract:Abstract The compelling evidence for an ocean beneath the ice shell of Europa makes it a high priority for astrobiological investigations. Future missions to the icy surface of this moon will query the plausibly Sulfur-rich materials for potential indications of the presence of life carried to the surface by mobile ice or partial melt. However, the potential for generation and preservation of biosignatures under cold, Sulfur-rich conditions has not previously been investigated, as there have not been suitable environments on Earth to study. Here, we describe the characterization of a range of biosignatures within potentially analogous Sulfur Deposits from the surface of an Arctic glacier at Borup Fiord Pass to evaluate whether evidence for microbial activities is produced and preserved within these Deposits. Optical and electron microscopy revealed microorganisms and extracellular materials. Elemental Sulfur (S0), the dominant mineralogy within field samples, is present as rhombic and needle-shaped minera...
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WHISPERS - Onboard SVM analysis of Hyperion data to detect Sulfur Deposits in Arctic regions
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009Co-Authors: Lukas Mandrake, Kiri L. Wagstaff, Damhnait Gleeson, Umaa Rebbapragada, Daniel Tran, Rebecca Castano, Steve Chien, Robert T. PappalardoAbstract:Onboard classification of remote sensing data can permit autonomous, intelligent scheduling decisions without ground interaction. In this study, we observe the Sulfur-rich Borup-Fiord glacial springs in Canada with the Hyperion instrument aboard the EO-1 spacecraft. This system offers an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of Sulfur compounds associated with the activity of microbial life. However, those results were severely limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. We also explore nonlinear SVM kernels as an extension of our onboard capability.
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Onboard SVM analysis of Hyperion data to detect Sulfur Deposits in Arctic regions
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009Co-Authors: Lukas Mandrake, Kiri L. Wagstaff, Damhnait Gleeson, Umaa Rebbapragada, Daniel Tran, Rebecca Castano, Steve Chien, Robert T. PappalardoAbstract:Onboard classification of remote sensing data can permit autonomous, intelligent scheduling decisions without ground interaction. In this study, we observe the Sulfur-rich Borup-Fiord glacial springs in Canada with the Hyperion instrument aboard the EO-1 spacecraft. This system offers an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of Sulfur compounds associated with the activity of microbial life. However, those results were severely limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. We also explore nonlinear SVM kernels as an extension of our onboard capability.
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Onboard Detection of Active Canadian Sulfur Springs: A Europa Analogue
2008Co-Authors: Rebecca Castano, Kiri L. Wagstaff, Damhnait Gleeson, Daniel Tran, Steve Chien, Robert T. Pappalardo, Lucas Scharenbroich, Baback Moghaddam, Benyang TangAbstract:We discuss a current, ongoing demonstration of insitu onboard detection in which the Earth Observing-1 spacecraft detects surface Sulfur Deposits that originate from underlying springs by distinguishing the Sulfur from the ice-rich glacial background, a good analogue for the Europan surface. In this paper, we describe the process of developing the onboard classifier for detecting the presence of Sulfur in a hyperspectral scene, including the use of a training/testing set that is not exhaustively labeled, i.e.not all true positives are marked, and the selection of 12, out of 242, Hyperion instrument wavelength bands to use in the onboard detector. This study aims to demonstrate the potential for future missions to capture short-lived science events, make decisions onboard, identify high priority data for downlink and perform onboard change detection. In the future, such capability could help maximize the science return of downlink bandwidth-limited missions, addressing a significant constraint in all deep-space missions.
Rebecca Castano - One of the best experts on this subject based on the ideXlab platform.
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Surface Sulfur Detection via Remote Sensing and Onboard Classification
ACM Transactions on Intelligent Systems and Technology, 2012Co-Authors: Lukas Mandrake, Kiri L. Wagstaff, Damhnait Gleeson, Umaa Rebbapragada, Daniel Tran, Steve Chien, Robert T. Pappalardo, David R. Thompson, Rebecca CastanoAbstract:Orbital remote sensing provides a powerful way to efficiently survey targets such as the Earth and other planets and moons for features of interest. One such feature of astrobiological relevance is the presence of surface Sulfur Deposits. These Deposits have been observed to be associated with microbial activity at the Borup Fiord glacial springs in Canada, a location that may provide an analogue to other icy environments such as Europa. This article evaluates automated classifiers for detecting Sulfur in remote sensing observations by the hyperion spectrometer on the EO-1 spacecraft. We determined that a data-driven machine learning solution was needed because the Sulfur could not be detected by simply matching observations to Sulfur lab spectra. We also evaluated several methods (manual and automated) for identifying the most relevant attributes (spectral wavelengths) needed for successful Sulfur detection. Our findings include (1) the Borup Fiord Sulfur Deposits were best modeled as containing two sub-populations: Sulfur on ice and Sulfur on rock; (2) as expected, classifiers using Gaussian kernels outperformed those based on linear kernels, and should be adopted when onboard computational constraints permit; and (3) Recursive Feature Elimination selected sensible and effective features for use in the computationally constrained environment onboard EO-1. This study helped guide the selection of algorithm parameters and configuration for the classification system currently operational on EO-1. Finally, we discuss implications for a similar onboard classification system for a future Europa orbiter.
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WHISPERS - Onboard SVM analysis of Hyperion data to detect Sulfur Deposits in Arctic regions
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009Co-Authors: Lukas Mandrake, Kiri L. Wagstaff, Damhnait Gleeson, Umaa Rebbapragada, Daniel Tran, Rebecca Castano, Steve Chien, Robert T. PappalardoAbstract:Onboard classification of remote sensing data can permit autonomous, intelligent scheduling decisions without ground interaction. In this study, we observe the Sulfur-rich Borup-Fiord glacial springs in Canada with the Hyperion instrument aboard the EO-1 spacecraft. This system offers an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of Sulfur compounds associated with the activity of microbial life. However, those results were severely limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. We also explore nonlinear SVM kernels as an extension of our onboard capability.
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Onboard SVM analysis of Hyperion data to detect Sulfur Deposits in Arctic regions
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009Co-Authors: Lukas Mandrake, Kiri L. Wagstaff, Damhnait Gleeson, Umaa Rebbapragada, Daniel Tran, Rebecca Castano, Steve Chien, Robert T. PappalardoAbstract:Onboard classification of remote sensing data can permit autonomous, intelligent scheduling decisions without ground interaction. In this study, we observe the Sulfur-rich Borup-Fiord glacial springs in Canada with the Hyperion instrument aboard the EO-1 spacecraft. This system offers an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of Sulfur compounds associated with the activity of microbial life. However, those results were severely limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. We also explore nonlinear SVM kernels as an extension of our onboard capability.
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Onboard Detection of Active Canadian Sulfur Springs: A Europa Analogue
2008Co-Authors: Rebecca Castano, Kiri L. Wagstaff, Damhnait Gleeson, Daniel Tran, Steve Chien, Robert T. Pappalardo, Lucas Scharenbroich, Baback Moghaddam, Benyang TangAbstract:We discuss a current, ongoing demonstration of insitu onboard detection in which the Earth Observing-1 spacecraft detects surface Sulfur Deposits that originate from underlying springs by distinguishing the Sulfur from the ice-rich glacial background, a good analogue for the Europan surface. In this paper, we describe the process of developing the onboard classifier for detecting the presence of Sulfur in a hyperspectral scene, including the use of a training/testing set that is not exhaustively labeled, i.e.not all true positives are marked, and the selection of 12, out of 242, Hyperion instrument wavelength bands to use in the onboard detector. This study aims to demonstrate the potential for future missions to capture short-lived science events, make decisions onboard, identify high priority data for downlink and perform onboard change detection. In the future, such capability could help maximize the science return of downlink bandwidth-limited missions, addressing a significant constraint in all deep-space missions.
Damhnait Gleeson - One of the best experts on this subject based on the ideXlab platform.
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Surface Sulfur Detection via Remote Sensing and Onboard Classification
ACM Transactions on Intelligent Systems and Technology, 2012Co-Authors: Lukas Mandrake, Kiri L. Wagstaff, Damhnait Gleeson, Umaa Rebbapragada, Daniel Tran, Steve Chien, Robert T. Pappalardo, David R. Thompson, Rebecca CastanoAbstract:Orbital remote sensing provides a powerful way to efficiently survey targets such as the Earth and other planets and moons for features of interest. One such feature of astrobiological relevance is the presence of surface Sulfur Deposits. These Deposits have been observed to be associated with microbial activity at the Borup Fiord glacial springs in Canada, a location that may provide an analogue to other icy environments such as Europa. This article evaluates automated classifiers for detecting Sulfur in remote sensing observations by the hyperion spectrometer on the EO-1 spacecraft. We determined that a data-driven machine learning solution was needed because the Sulfur could not be detected by simply matching observations to Sulfur lab spectra. We also evaluated several methods (manual and automated) for identifying the most relevant attributes (spectral wavelengths) needed for successful Sulfur detection. Our findings include (1) the Borup Fiord Sulfur Deposits were best modeled as containing two sub-populations: Sulfur on ice and Sulfur on rock; (2) as expected, classifiers using Gaussian kernels outperformed those based on linear kernels, and should be adopted when onboard computational constraints permit; and (3) Recursive Feature Elimination selected sensible and effective features for use in the computationally constrained environment onboard EO-1. This study helped guide the selection of algorithm parameters and configuration for the classification system currently operational on EO-1. Finally, we discuss implications for a similar onboard classification system for a future Europa orbiter.
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Biosignature Detection at an Arctic Analog to Europa
Astrobiology, 2012Co-Authors: Damhnait Gleeson, Robert T. Pappalardo, Mark S. Anderson, Stephen E. Grasby, Randall E. Mielke, Katherine E. Wright, Alexis S. TempletonAbstract:Abstract The compelling evidence for an ocean beneath the ice shell of Europa makes it a high priority for astrobiological investigations. Future missions to the icy surface of this moon will query the plausibly Sulfur-rich materials for potential indications of the presence of life carried to the surface by mobile ice or partial melt. However, the potential for generation and preservation of biosignatures under cold, Sulfur-rich conditions has not previously been investigated, as there have not been suitable environments on Earth to study. Here, we describe the characterization of a range of biosignatures within potentially analogous Sulfur Deposits from the surface of an Arctic glacier at Borup Fiord Pass to evaluate whether evidence for microbial activities is produced and preserved within these Deposits. Optical and electron microscopy revealed microorganisms and extracellular materials. Elemental Sulfur (S0), the dominant mineralogy within field samples, is present as rhombic and needle-shaped minera...
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WHISPERS - Onboard SVM analysis of Hyperion data to detect Sulfur Deposits in Arctic regions
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009Co-Authors: Lukas Mandrake, Kiri L. Wagstaff, Damhnait Gleeson, Umaa Rebbapragada, Daniel Tran, Rebecca Castano, Steve Chien, Robert T. PappalardoAbstract:Onboard classification of remote sensing data can permit autonomous, intelligent scheduling decisions without ground interaction. In this study, we observe the Sulfur-rich Borup-Fiord glacial springs in Canada with the Hyperion instrument aboard the EO-1 spacecraft. This system offers an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of Sulfur compounds associated with the activity of microbial life. However, those results were severely limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. We also explore nonlinear SVM kernels as an extension of our onboard capability.
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Onboard SVM analysis of Hyperion data to detect Sulfur Deposits in Arctic regions
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009Co-Authors: Lukas Mandrake, Kiri L. Wagstaff, Damhnait Gleeson, Umaa Rebbapragada, Daniel Tran, Rebecca Castano, Steve Chien, Robert T. PappalardoAbstract:Onboard classification of remote sensing data can permit autonomous, intelligent scheduling decisions without ground interaction. In this study, we observe the Sulfur-rich Borup-Fiord glacial springs in Canada with the Hyperion instrument aboard the EO-1 spacecraft. This system offers an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of Sulfur compounds associated with the activity of microbial life. However, those results were severely limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. We also explore nonlinear SVM kernels as an extension of our onboard capability.
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Onboard Detection of Active Canadian Sulfur Springs: A Europa Analogue
2008Co-Authors: Rebecca Castano, Kiri L. Wagstaff, Damhnait Gleeson, Daniel Tran, Steve Chien, Robert T. Pappalardo, Lucas Scharenbroich, Baback Moghaddam, Benyang TangAbstract:We discuss a current, ongoing demonstration of insitu onboard detection in which the Earth Observing-1 spacecraft detects surface Sulfur Deposits that originate from underlying springs by distinguishing the Sulfur from the ice-rich glacial background, a good analogue for the Europan surface. In this paper, we describe the process of developing the onboard classifier for detecting the presence of Sulfur in a hyperspectral scene, including the use of a training/testing set that is not exhaustively labeled, i.e.not all true positives are marked, and the selection of 12, out of 242, Hyperion instrument wavelength bands to use in the onboard detector. This study aims to demonstrate the potential for future missions to capture short-lived science events, make decisions onboard, identify high priority data for downlink and perform onboard change detection. In the future, such capability could help maximize the science return of downlink bandwidth-limited missions, addressing a significant constraint in all deep-space missions.
Lukas Mandrake - One of the best experts on this subject based on the ideXlab platform.
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Surface Sulfur Detection via Remote Sensing and Onboard Classification
ACM Transactions on Intelligent Systems and Technology, 2012Co-Authors: Lukas Mandrake, Kiri L. Wagstaff, Damhnait Gleeson, Umaa Rebbapragada, Daniel Tran, Steve Chien, Robert T. Pappalardo, David R. Thompson, Rebecca CastanoAbstract:Orbital remote sensing provides a powerful way to efficiently survey targets such as the Earth and other planets and moons for features of interest. One such feature of astrobiological relevance is the presence of surface Sulfur Deposits. These Deposits have been observed to be associated with microbial activity at the Borup Fiord glacial springs in Canada, a location that may provide an analogue to other icy environments such as Europa. This article evaluates automated classifiers for detecting Sulfur in remote sensing observations by the hyperion spectrometer on the EO-1 spacecraft. We determined that a data-driven machine learning solution was needed because the Sulfur could not be detected by simply matching observations to Sulfur lab spectra. We also evaluated several methods (manual and automated) for identifying the most relevant attributes (spectral wavelengths) needed for successful Sulfur detection. Our findings include (1) the Borup Fiord Sulfur Deposits were best modeled as containing two sub-populations: Sulfur on ice and Sulfur on rock; (2) as expected, classifiers using Gaussian kernels outperformed those based on linear kernels, and should be adopted when onboard computational constraints permit; and (3) Recursive Feature Elimination selected sensible and effective features for use in the computationally constrained environment onboard EO-1. This study helped guide the selection of algorithm parameters and configuration for the classification system currently operational on EO-1. Finally, we discuss implications for a similar onboard classification system for a future Europa orbiter.
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WHISPERS - Onboard SVM analysis of Hyperion data to detect Sulfur Deposits in Arctic regions
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009Co-Authors: Lukas Mandrake, Kiri L. Wagstaff, Damhnait Gleeson, Umaa Rebbapragada, Daniel Tran, Rebecca Castano, Steve Chien, Robert T. PappalardoAbstract:Onboard classification of remote sensing data can permit autonomous, intelligent scheduling decisions without ground interaction. In this study, we observe the Sulfur-rich Borup-Fiord glacial springs in Canada with the Hyperion instrument aboard the EO-1 spacecraft. This system offers an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of Sulfur compounds associated with the activity of microbial life. However, those results were severely limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. We also explore nonlinear SVM kernels as an extension of our onboard capability.
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Onboard SVM analysis of Hyperion data to detect Sulfur Deposits in Arctic regions
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009Co-Authors: Lukas Mandrake, Kiri L. Wagstaff, Damhnait Gleeson, Umaa Rebbapragada, Daniel Tran, Rebecca Castano, Steve Chien, Robert T. PappalardoAbstract:Onboard classification of remote sensing data can permit autonomous, intelligent scheduling decisions without ground interaction. In this study, we observe the Sulfur-rich Borup-Fiord glacial springs in Canada with the Hyperion instrument aboard the EO-1 spacecraft. This system offers an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of Sulfur compounds associated with the activity of microbial life. However, those results were severely limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. We also explore nonlinear SVM kernels as an extension of our onboard capability.
Kiri L. Wagstaff - One of the best experts on this subject based on the ideXlab platform.
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Surface Sulfur Detection via Remote Sensing and Onboard Classification
ACM Transactions on Intelligent Systems and Technology, 2012Co-Authors: Lukas Mandrake, Kiri L. Wagstaff, Damhnait Gleeson, Umaa Rebbapragada, Daniel Tran, Steve Chien, Robert T. Pappalardo, David R. Thompson, Rebecca CastanoAbstract:Orbital remote sensing provides a powerful way to efficiently survey targets such as the Earth and other planets and moons for features of interest. One such feature of astrobiological relevance is the presence of surface Sulfur Deposits. These Deposits have been observed to be associated with microbial activity at the Borup Fiord glacial springs in Canada, a location that may provide an analogue to other icy environments such as Europa. This article evaluates automated classifiers for detecting Sulfur in remote sensing observations by the hyperion spectrometer on the EO-1 spacecraft. We determined that a data-driven machine learning solution was needed because the Sulfur could not be detected by simply matching observations to Sulfur lab spectra. We also evaluated several methods (manual and automated) for identifying the most relevant attributes (spectral wavelengths) needed for successful Sulfur detection. Our findings include (1) the Borup Fiord Sulfur Deposits were best modeled as containing two sub-populations: Sulfur on ice and Sulfur on rock; (2) as expected, classifiers using Gaussian kernels outperformed those based on linear kernels, and should be adopted when onboard computational constraints permit; and (3) Recursive Feature Elimination selected sensible and effective features for use in the computationally constrained environment onboard EO-1. This study helped guide the selection of algorithm parameters and configuration for the classification system currently operational on EO-1. Finally, we discuss implications for a similar onboard classification system for a future Europa orbiter.
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WHISPERS - Onboard SVM analysis of Hyperion data to detect Sulfur Deposits in Arctic regions
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009Co-Authors: Lukas Mandrake, Kiri L. Wagstaff, Damhnait Gleeson, Umaa Rebbapragada, Daniel Tran, Rebecca Castano, Steve Chien, Robert T. PappalardoAbstract:Onboard classification of remote sensing data can permit autonomous, intelligent scheduling decisions without ground interaction. In this study, we observe the Sulfur-rich Borup-Fiord glacial springs in Canada with the Hyperion instrument aboard the EO-1 spacecraft. This system offers an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of Sulfur compounds associated with the activity of microbial life. However, those results were severely limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. We also explore nonlinear SVM kernels as an extension of our onboard capability.
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Onboard SVM analysis of Hyperion data to detect Sulfur Deposits in Arctic regions
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009Co-Authors: Lukas Mandrake, Kiri L. Wagstaff, Damhnait Gleeson, Umaa Rebbapragada, Daniel Tran, Rebecca Castano, Steve Chien, Robert T. PappalardoAbstract:Onboard classification of remote sensing data can permit autonomous, intelligent scheduling decisions without ground interaction. In this study, we observe the Sulfur-rich Borup-Fiord glacial springs in Canada with the Hyperion instrument aboard the EO-1 spacecraft. This system offers an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of Sulfur compounds associated with the activity of microbial life. However, those results were severely limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. We also explore nonlinear SVM kernels as an extension of our onboard capability.
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Onboard Detection of Active Canadian Sulfur Springs: A Europa Analogue
2008Co-Authors: Rebecca Castano, Kiri L. Wagstaff, Damhnait Gleeson, Daniel Tran, Steve Chien, Robert T. Pappalardo, Lucas Scharenbroich, Baback Moghaddam, Benyang TangAbstract:We discuss a current, ongoing demonstration of insitu onboard detection in which the Earth Observing-1 spacecraft detects surface Sulfur Deposits that originate from underlying springs by distinguishing the Sulfur from the ice-rich glacial background, a good analogue for the Europan surface. In this paper, we describe the process of developing the onboard classifier for detecting the presence of Sulfur in a hyperspectral scene, including the use of a training/testing set that is not exhaustively labeled, i.e.not all true positives are marked, and the selection of 12, out of 242, Hyperion instrument wavelength bands to use in the onboard detector. This study aims to demonstrate the potential for future missions to capture short-lived science events, make decisions onboard, identify high priority data for downlink and perform onboard change detection. In the future, such capability could help maximize the science return of downlink bandwidth-limited missions, addressing a significant constraint in all deep-space missions.
