10MaximumLikelyhoodClassifierPowerpnts

10MaximumLikelyhoodClassifierPowerpnts - The Maximum...

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The Maximum Likelyhood Classifier • Many pixels are actuall between two spectral signatures: Mixels • Must force into a class • Other classifiers assume mixels have equal likelyhood of going into any class • Assume each class is an equal percentage of the image • Maximum Likelyhood classifier helps adjust mixel assignements to account for mixed land cover percentages
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Example Water 20% Forest 30% Urban 40% Fields 10% With maximum likelyhood the number of mixels assigned to each class is based on the percentage of the scene that should be in each category (prior probabilities) Water 20% Forest 30% Urban 40% Fields 10% Mixels assigned equally to each category Too many mixels assigned to Fields Not enough assigned to urban
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Disadvantage of Maximum Likelyhood • Uses means and variences of each training field • Assumes a gaussian distribution of training field pixels • Must examine histogram of training fields • Can only use single peaked training fields
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This note was uploaded on 12/21/2010 for the course GIS 4037c taught by Professor Roberts during the Spring '10 term at FAU.

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10MaximumLikelyhoodClassifierPowerpnts - The Maximum...

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