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Activity Number:
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15
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 6, 2006 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #306136 |
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Title:
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Weighted Model-Based Clustering for Remote Sensing Image Analysis
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Author(s):
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Joseph Richards*+ and Johanna Hardin
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Companies:
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Carnegie Mellon University and Pomona College
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Address:
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2718 Allenton Ave., Hacienda Heights, CA, 91745,
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Keywords:
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weighted likelihood ; mixture model ; EM algorithm ; large data sets ; geologic map
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Abstract:
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We introduce a weighted method of clustering the individual units of a segmented image. Our weighted model-based clustering method (WMBC) employs a weighted likelihood and assigns fixed weights to each unit corresponding to the number of pixels located within the unit. To conquer computing time and space issues, WMBC uses the means and standard deviations of the pixel values within each unit. With both simulated and real datasets, we show WMBC is more accurate than standard model-based clustering.
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