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Dealing with Data Corruption in Remote Sensing

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Advances in Intelligent Data Analysis VI (IDA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3646))

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Abstract

Remote sensing has resulted in repositories of data that grow at a pace much faster than can be readily analyzed. One of the obstacles in dealing with remotely sensed data and others is the variable quality of the data. Instrument failures can result in entire missing observation cycles, while cloud cover frequently results in missing or distorted values. We investigated the use of several methods that automatically deal with corruptions in the data. These include robust measures which avoid overfitting, filtering which discards the corrupted instances, and polishing by which the corrupted elements are fitted with more appropriate values. We applied such methods to a data set of vegetation indices and land cover type assembled from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) data collection.

This work was supported by NASA NCC2-1239, NNA04CK88A and ONR N00014-03-1-0516.

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Author information

Authors and Affiliations

  1. Institute for Human and Machine Cognition, 40 South Alcaniz Street, Pensacola, FL, 32502, USA

    Choh Man Teng

Authors
  1. Choh Man Teng
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Editor information

Editors and Affiliations

  1. Institute for Information Technology, National Research Council Canada, Ottawa, Canada

    A. Fazel Famili

  2. LIACS, Leiden University, The Netherlands

    Joost N. Kok

  3. IFM, Linköping University, SE-58183, Linköping, Sweden

    José M. Peña

  4. Department of Computer Science, Universiteit Utrecht,  

    Arno Siebes

  5. Utrecht University, TB Utrecht,, P.O. box 80 089, NL-3508, the Netherlands

    Ad Feelders

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© 2005 Springer-Verlag Berlin Heidelberg

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Teng, C.M. (2005). Dealing with Data Corruption in Remote Sensing. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds) Advances in Intelligent Data Analysis VI. IDA 2005. Lecture Notes in Computer Science, vol 3646. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552253_41

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  • DOI: https://doi.org/10.1007/11552253_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28795-7

  • Online ISBN: 978-3-540-31926-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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