Article Type:
Research Article

Optimization of an Instance-Based GOES Cloud Classification Algorithm

Richard L. Bankert Naval Research Laboratory, Monterey, California

Search for other papers by Richard L. Bankert in
Current site
Google Scholar
PubMed Close
and
Robert H. Wade Science Applications International Corporation, Monterey, California

Search for other papers by Robert H. Wade in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Jan 2007
DOI:
https://doi.org/10.1175/JAM2451.1
Page(s):
36–49
Restricted access

Abstract

An instance-based nearest-neighbor algorithm was developed for a Geostationary Operational Environmental Satellite (GOES) cloud classifier. Expert-labeled samples serve as the training sets for the various GOES image classification scenes. The initial implementation of the classifier using the complete set of available training samples has proven to be an inefficient method for real-time image classifications, requiring long computational run times and significant computer resources. A variety of training-set reduction methods were examined to find smaller training sets that provide quicker classifier run times with minimal reduction in classifier testing set accuracy. General differences within real-time image classifications as a result of using the various reduction methods were also analyzed. The fast condensed nearest-neighbor (FCNN) method reduced the size of the individual training sets by 68.3% (fourfold cross-validation testing average) while the average overall accuracy of the testing sets decreased by only 4.1%. Training sets resulting from these reduction methods were also applied within a real-time classifier using a one-nearest-neighbor subroutine. Using the FCNN-reduced set, the subroutine run time on a 30° latitude × 30° longitude image (GOES-10 daytime) with 11 289 600 total pixels decreased by an average of 60.7%.

Corresponding author address: Richard Bankert, Naval Research Laboratory, 7 Grace Hopper Ave., Monterey, CA 93943-5502. Email: rich.bankert@nrlmry.navy.mil

Abstract

An instance-based nearest-neighbor algorithm was developed for a Geostationary Operational Environmental Satellite (GOES) cloud classifier. Expert-labeled samples serve as the training sets for the various GOES image classification scenes. The initial implementation of the classifier using the complete set of available training samples has proven to be an inefficient method for real-time image classifications, requiring long computational run times and significant computer resources. A variety of training-set reduction methods were examined to find smaller training sets that provide quicker classifier run times with minimal reduction in classifier testing set accuracy. General differences within real-time image classifications as a result of using the various reduction methods were also analyzed. The fast condensed nearest-neighbor (FCNN) method reduced the size of the individual training sets by 68.3% (fourfold cross-validation testing average) while the average overall accuracy of the testing sets decreased by only 4.1%. Training sets resulting from these reduction methods were also applied within a real-time classifier using a one-nearest-neighbor subroutine. Using the FCNN-reduced set, the subroutine run time on a 30° latitude × 30° longitude image (GOES-10 daytime) with 11 289 600 total pixels decreased by an average of 60.7%.

Corresponding author address: Richard Bankert, Naval Research Laboratory, 7 Grace Hopper Ave., Monterey, CA 93943-5502. Email: rich.bankert@nrlmry.navy.mil

Save
Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

  • Aha, D. W., D. Kibler, and M. K. Albert, 1991: Instance-based learning algorithms. Mach. Learn., 6 , 37–66.

  • Angiulli, F., 2005: Fast condensed nearest neighbor rule. Proc. 22d Int. Conf. on Machine Learning, Bonn, Germany, International Machine Learning Society, 25–32.

  • Bankert, R. L., and D. W. Aha, 1996: Improvement to a neural network cloud classifier. J. Appl. Meteor., 35 , 2036–2039.

  • Baum, B. A., V. Tovinkere, J. Titlow, and R. M. Welch, 1997: Automated cloud classification of global AVHRR data using a fuzzy logic approach. J. Appl. Meteor., 36 , 1519–1540.

    • Search Google Scholar
    • Export Citation

  • Cover, T. M., and P. E. Hart, 1967: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory, 13 , 21–27.

  • Derrien, M., and H. Le Gleau, 1999: Cloud classification extracted from AVHRR and GOES imagery. Proc. 1999 EUMETSAT Meteorological Satellite Data User’s Conf., Copenhagen, Denmark, EUMETSAT, 545–553.

  • Hart, P. E., 1968: The condensed nearest neighbor rule. IEEE Trans. Inf. Theory, 14 , 515–516.

  • Hong, Y., K-L. Hsu, S. Sorooshian, and X. Gao, 2004: Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteor., 43 , 1834–1853.

    • Search Google Scholar
    • Export Citation

  • Lee, Y., G. Wahba, and S. A. Ackerman, 2004: Cloud classification of satellite radiance data by multicategory support vector machines. J. Atmos. Oceanic Technol., 21 , 159–169.

    • Search Google Scholar
    • Export Citation

  • Lewis, H. G., S. Cote, and A. R. L. Tatnall, 1997: Determination of spatial and temporal characteristics as an aid to neural network cloud classification. Int. J. Remote Sens., 18 , 899–915.

    • Search Google Scholar
    • Export Citation

  • Li, J., W. P. Menzel, Z. Yang, R. A. Frey, and S. A. Ackerman, 2003: High-spatial-resolution surface and cloud-type classification from MODIS multispectral band measurements. J. Appl. Meteor., 42 , 204–226.

    • Search Google Scholar
    • Export Citation

  • Li, Z., J. Li, P. Menzel, and T. J. Schmit, 2005: Imager capability on cloud classification using MODIS. Extended Abstracts, 21st Int. Conf. on Interactive Information Processing for Meteorology, Oceanography, and Hydrology, San Diego, CA, Amer. Meteor. Soc., CD-ROM, P1.30.

  • Liu, G. S., J. A. Curry, and R. S. Sheu, 1995: Classification of clouds over the western equatorial Pacific Ocean using combined infrared and microwave satellite data. J. Geophys. Res., 100 , 13811–13826.

    • Search Google Scholar
    • Export Citation

  • Miller, S. W., and W. J. Emery, 1997: An automated neural network cloud classifier for use over land and ocean surfaces. J. Appl. Meteor., 36 , 1346–1362.

    • Search Google Scholar
    • Export Citation

  • Pankiewicz, G. S., 1995: Pattern recognition techniques for identification of cloud and cloud systems. Meteor. Appl., 2 , 257–271.

  • Pavolonis, M. J., A. K. Heidinger, and T. Uttal, 2005: Daytime global cloud typing from AVHRR and VIIRS: Algorithm description, validation, and comparisons. J. Appl. Meteor., 44 , 804–826.

    • Search Google Scholar
    • Export Citation

  • Tag, P. M., R. L. Bankert, and L. R. Brody, 2000: An AVHRR multiple cloud-type classification package. J. Appl. Meteor., 39 , 125–134.

  • Tian, B., M. A. Shaikh, M. R. Azimi-Sadjadi, T. H. Vonder Haar, and D. L. Reinke, 1999: A study of cloud classification with neural networks using spectral and textural features. IEEE Trans. Neural Networks, 10 , 138–151.

    • Search Google Scholar
    • Export Citation

  • Uddstrom, M. J., and W. R. Gray, 1996: Satellite cloud classification and rain-rate estimation using multispectral radiances and measures of spatial texture. J. Appl. Meteor., 35 , 839–858.

    • Search Google Scholar
    • Export Citation

  • Welch, R. M., S. K. Sengupta, A. K. Goroch, P. Rabindra, N. Rangaraj, and M. S. Navar, 1992: Polar cloud and surface classification using AVHRR imagery: An intercomparison of methods. J. Appl. Meteor., 31 , 405–420.

    • Search Google Scholar
    • Export Citation

  • Wilson, D. R., and T. R. Martinez, 2000: Reduction techniques for instance-based learning algorithms. Mach. Learn., 38 , 257–286.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 641 459 192
PDF Downloads 122 24 3