• Aha, D. W., and R. L. Bankert, 1995: A comparative evaluation of sequential feature selection algorithms. Learning from Data: Artificial Intelligence and Statistics V, D. Fisher and H.-J. Lenz, Eds., Springer-Verlag, 199–206.

  • Allen, R. C., Jr., P. A. Durkee, and C. H. Wash, 1990: Snow/cloud discrimination with multispectral satellite measurements. J. Appl. Meteor.,29, 994–1004.

  • Bankert, R. L., 1994: Cloud classification of AVHRR imagery in maritime regions using a probabilistic neural network. J. Appl. Meteor.,33, 909–918.

  • Bankert, R. L., and D. W. Aha, 1995: Automated identification of cloud patterns in satellite imagery. Preprints, 14th Conf. on Weather Analysis and Forecasting, Dallas, TX, Amer. Meteor. Soc., 313–316.

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

  • Bankert, R. L., and P. M. Tag, 1998: Using SSM/I data and computer vision to estimate tropical cyclone intensity. Preprints, Ninth Conf. on Satellite Meteorology and Oceanography, Paris, France, Amer. Meteor. Soc., 226–229.

  • 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.

  • Cheeseman, P., J. Kelly, M. Self, J. Stutz, W. Taylor, and D. Freeman, 1988: Autoclass: A Bayesian classification system. Proc. Fifth Int. Conf. on Machine Learning, Ann Arbor, MI, Cognitive Science and Machine Intelligence Laboratory of the University of Michigan, 54–63.

  • Crosiar, C. L., 1993: An AVHRR cloud classification database typed by experts. Naval Research Laboratory Memo. Rep. NRL/MR/7531-93-7207, Monterey, CA, 31 pp. [Available from Naval Research Laboratory, 7 Grace Hopper Ave., Monterey, CA 93943-5502.].

  • Desbois, M., G. Seze, and G. Szejwach, 1982: Automatic classification of clouds on Meteosat imagery: Application to high-level clouds. J. Appl. Meteor.,21, 401–412.

  • Ebert, E., 1987: A pattern recognition technique for distinguishing surface and cloud types in the polar regions. J. Climate Appl. Meteor.,26, 1412–1427.

  • Ebert, E., 1989: Analysis of polar clouds from satellite imagery using pattern recognition and a statistical cloud analysis scheme. J. Appl. Meteor.,28, 382–399.

  • Garand, L., 1988: Automated recognition of oceanic cloud patterns. Part I: Methodology and application to cloud climatology. J. Climate,1, 20–39.

  • Gordon, D. F., P. M. Tag, and R. L. Bankert, 1995: Unsupervised classification procedures applied to satellite cloud data. Naval Research Laboratory Internal Rep. AIC-95-005, Washington, D.C., 6 pp. [Available from Naval Research Laboratory, Code 5514, Washington, DC 20375-5337.].

  • Inoue, T., 1987: A cloud type classification with NOAA-7 split-window measurements. J. Geophys. Res.,92, 3991–4000.

  • Key, J., 1990: Cloud cover analysis with arctic AVHRR data. Part II: Classification with spectral and textural measures. J. Geophys. Res.,95, 7661–7675.

  • Key, J., J. A. Maslanik, and A. J. Schweiger, 1989: Classification of merged AVHRR and SMMR arctic data with neural networks. Photogramm. Eng. Remote Sens.,55, 1331–1338.

  • Knottenberg, H., and E. Raschke, 1982: On the discrimination of water and ice clouds in multispectral AVHRR data. Ann. Meteor.,18, 145–147.

  • Lee, J., R. C. Weger, S. K. Sengupta, and R. M. Welch, 1990: A neural network approach to cloud classification. IEEE Trans. Geosci. Remote Sens.,28, 846–855.

  • 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.

  • Lubin, D., and E. Morrow, 1998: Evaluation of an AVHRR cloud detection and classification method over the central Arctic Ocean. J. Appl. Meteor.,37, 166–183.

  • MacQueen, J., 1967: Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. I, L. M. LeCam and J. Neyman, Eds., University of California Press, 281–297.

  • 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.

  • Peak, J. E., and P. M. Tag, 1992: Toward automated interpretation of satellite imagery for Navy shipboard applications. Bull. Amer. Meteor. Soc.,73, 995–1008.

  • Peak, J. E., and P. M. Tag, 1994: Segmentation of satellite imagery using hierarchical thresholding and neural networks. J. Appl. Meteor.,33, 605–616.

  • Shenk, W. E., R. J. Holub, and R. A. Neff, 1976: A multispectral cloud type identification method developed for tropical ocean areas with Nimbus-3 MRIR measurements. Mon. Wea. Rev.,104, 284–291.

  • Tsonis, A. A., 1984: On the separability of various classes from the GOES visible and infrared data. J. Climate Appl. Meteor.,23, 1393–1410.

  • 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.

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An AVHRR Multiple Cloud-Type Classification Package

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  • 1 Naval Research Laboratory, Monterey, California
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Abstract

Using imagery from NOAA’s Advanced Very High Resolution Radiometer (AVHRR) orbiting sensor, one of the authors (RLB) earlier developed a probabilistic neural network cloud classifier valid over the world’s maritime regions. Since then, the authors have created a database of nearly 8000 16 × 16 pixel cloud samples (from 13 Northern Hemispheric land regions) independently classified by three experts. From these samples, 1605 were of sufficient quality to represent 11 conventional cloud types (including clear). This database serves as the training and testing samples for developing a classifier valid over land. Approximately 200 features, calculated from a visible and an infrared channel, form the basis for the computer vision analysis. Using a 1–nearest neighbor classifier, meshed with a feature selection method using backward sequential selection, the authors select the fewest features that maximize classification accuracy. In a leave-one-out test, overall classification accuracies range from 86% to 78% for the water and land classifiers, with accuracies at 88% or greater for general height-dependent groupings. Details of the databases, feature selection method, and classifiers, as well as example simulations, are presented.

Corresponding author address: Dr. Paul M. Tag, Department of the Navy, Naval Research Lab, Monterey, CA 93943-5502.

tag@nrlmry.navy.mil

Abstract

Using imagery from NOAA’s Advanced Very High Resolution Radiometer (AVHRR) orbiting sensor, one of the authors (RLB) earlier developed a probabilistic neural network cloud classifier valid over the world’s maritime regions. Since then, the authors have created a database of nearly 8000 16 × 16 pixel cloud samples (from 13 Northern Hemispheric land regions) independently classified by three experts. From these samples, 1605 were of sufficient quality to represent 11 conventional cloud types (including clear). This database serves as the training and testing samples for developing a classifier valid over land. Approximately 200 features, calculated from a visible and an infrared channel, form the basis for the computer vision analysis. Using a 1–nearest neighbor classifier, meshed with a feature selection method using backward sequential selection, the authors select the fewest features that maximize classification accuracy. In a leave-one-out test, overall classification accuracies range from 86% to 78% for the water and land classifiers, with accuracies at 88% or greater for general height-dependent groupings. Details of the databases, feature selection method, and classifiers, as well as example simulations, are presented.

Corresponding author address: Dr. Paul M. Tag, Department of the Navy, Naval Research Lab, Monterey, CA 93943-5502.

tag@nrlmry.navy.mil

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