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Dynamic Landmarking for Surface Feature Identification and Change Detection

Authors:

Kiri L. Wagstaff,

Julian Panetta,

Ronald Greeley,

Mary Pendleton Hoffer,

Norbert SchörghoferAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 3, Issue 3

Article No.: 49, Pages 1 - 22

https://doi.org/10.1145/2168752.2168763

Published: 01 May 2012 Publication History

Abstract

Given the large volume of images being sent back from remote spacecraft, there is a need for automated analysis techniques that can quickly identify interesting features in those images. Feature identification in individual images and automated change detection in multiple images of the same target are valuable for scientific studies and can inform subsequent target selection. We introduce a new approach to orbital image analysis called dynamic landmarking. It focuses on the identification and comparison of visually salient features in images. We have evaluated this approach on images collected by five Mars orbiters. These evaluations were motivated by three scientific goals: to study fresh impact craters, dust devil tracks, and dark slope streaks on Mars. In the process we also detected a different kind of surface change that may indicate seasonally exposed bedforms. These experiences also point the way to how this approach could be used in an onboard setting to analyze and prioritize data as it is collected.

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Cited By

Zhang FHao WYe MTu ZLi F(2024)Automatic Change Detection of Planetary Striped Landform on Mars SurfaceIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2024.337101517(5884-5898)Online publication date: 2024
https://doi.org/10.1109/JSTARS.2024.3371015
Nagle-McNaughton TMcClanahan TScuderi L(2020)PlaNet: A Neural Network for Detecting Transverse Aeolian Ridges on MarsRemote Sensing10.3390/rs1221360712:21(3607)Online publication date: 3-Nov-2020
https://doi.org/10.3390/rs12213607
Marchetti YLightholder JJunkins ECross MMandrake LFraeman A(2020)Barefoot Rover: a Sensor-Embedded Rover Wheel Demonstrating In-Situ Engineering and Science Extractions using Machine Learning2020 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA40945.2020.9197500(6000-6006)Online publication date: May-2020
https://doi.org/10.1109/ICRA40945.2020.9197500
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Index Terms

Dynamic Landmarking for Surface Feature Identification and Change Detection
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Multimedia information systems
      1. Multimedia databases

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 3, Issue 3

May 2012

384 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2168752

Issue’s Table of Contents

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 May 2012

Accepted: 01 April 2011

Revised: 01 April 2011

Received: 01 February 2011

Published in TIST Volume 3, Issue 3

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Cited By

Zhang FHao WYe MTu ZLi F(2024)Automatic Change Detection of Planetary Striped Landform on Mars SurfaceIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2024.337101517(5884-5898)Online publication date: 2024
https://doi.org/10.1109/JSTARS.2024.3371015
Nagle-McNaughton TMcClanahan TScuderi L(2020)PlaNet: A Neural Network for Detecting Transverse Aeolian Ridges on MarsRemote Sensing10.3390/rs1221360712:21(3607)Online publication date: 3-Nov-2020
https://doi.org/10.3390/rs12213607
Marchetti YLightholder JJunkins ECross MMandrake LFraeman A(2020)Barefoot Rover: a Sensor-Embedded Rover Wheel Demonstrating In-Situ Engineering and Science Extractions using Machine Learning2020 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA40945.2020.9197500(6000-6006)Online publication date: May-2020
https://doi.org/10.1109/ICRA40945.2020.9197500
Doran GLu SLiukis MMandrake LRebbapragada UWagstaff KYoung JLangert EBraunegg AHorton PJeong DTrockman A(2020)COSMIC: Content-based Onboard Summarization to Monitor Infrequent Change2020 IEEE Aerospace Conference10.1109/AERO47225.2020.9172337(1-12)Online publication date: Mar-2020
https://doi.org/10.1109/AERO47225.2020.9172337
Wardlaw JSprinks JHoughton RMuller JSidiropoulos PBamford SMarsh S(2018)Comparing experts and novices in Martian surface feature change detection and identificationInternational Journal of Applied Earth Observation and Geoinformation10.1016/j.jag.2017.05.01464(354-364)Online publication date: Feb-2018
https://doi.org/10.1016/j.jag.2017.05.014
Chien SDoubleday JThompson DWagstaff KBellardo JFrancis CBaumgarten EWilliams AYee EStanton EPiug-Suari J(2017)Onboard Autonomy on the Intelligent Payload Experiment CubeSat MissionJournal of Aerospace Information Systems10.2514/1.I01038614:6(307-315)Online publication date: Jun-2017
https://doi.org/10.2514/1.I010386
Wang YDi KXin XWan W(2017)Automatic detection of Martian dark slope streaks by machine learning using HiRISE imagesISPRS Journal of Photogrammetry and Remote Sensing10.1016/j.isprsjprs.2017.04.014129(12-20)Online publication date: Jul-2017
https://doi.org/10.1016/j.isprsjprs.2017.04.014
Xin XDi KWang YWan WYue Z(2017)Automated detection of new impact sites on Martian surface from HiRISE imagesAdvances in Space Research10.1016/j.asr.2017.06.04460:7(1557-1569)Online publication date: Oct-2017
https://doi.org/10.1016/j.asr.2017.06.044
Cheng FChen BHuang S(2015)A Hybrid Background Subtraction Method with Background and Foreground Candidates DetectionACM Transactions on Intelligent Systems and Technology10.1145/27464097:1(1-14)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1145/2746409
Doubleday JChien SNorton CWagstaff KThompson DBellardo JFrancis CBaumgarten E(2015)Autonomy for remote sensing — Experiences from the IPEX CubeSat2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)10.1109/IGARSS.2015.7327033(5308-5311)Online publication date: Jul-2015
https://doi.org/10.1109/IGARSS.2015.7327033
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