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Enhancing Mastcam Images for Mars Rover Mission

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Advances in Neural Networks - ISNN 2017 (ISNN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10262))

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Abstract

This paper summarizes some new results in improving the left Mastcam images of the Mars Science Laboratory (MSL) onboard the Mars rover Curiosity. There are two multispectral Mastcam imagers, having 9 bands in each. The left imager has wide field of view, but low resolution whereas the right imager is just the opposite. Our goal is to investigate the possibility of fusing the left and right images to form high spatial resolution and high spectral resolution data cube so that stereo images and data clustering performance can be improved. Many pansharpening algorithms have been investigated. Actual Mastcam images were used in our experiments. Preliminary results indicate that the pansharpened images can indeed enhance the data clustering performance using both objective and subjective evaluations.

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Acknowledgements

This research was supported by NASA under contract NNX16CP38P.

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Authors and Affiliations

  1. Applied Research LLC, Rockville, MD, USA

    Minh Dao, Chiman Kwan & Bulent Ayhan

  2. Arizona State University, Tempe, AZ, USA

    James F. Bell

Authors
  1. Minh Dao
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  2. Chiman Kwan
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  3. Bulent Ayhan
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  4. James F. Bell
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Corresponding author

Correspondence to Chiman Kwan .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

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Dao, M., Kwan, C., Ayhan, B., Bell, J.F. (2017). Enhancing Mastcam Images for Mars Rover Mission. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_24

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  • DOI: https://doi.org/10.1007/978-3-319-59081-3_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59080-6

  • Online ISBN: 978-3-319-59081-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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