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Erdas Imagine Classification Accuracy Enhanced using Filling

Erdas Imagine Classification Accuracy Enhanced using Filling

This video demonstrates how to use fill tool to manually select a miss-classified area and assign it class. Unsupervised classification often results miss-classified or unclassified areas. Erdas Imagine provide Filling tool to manually select these areas and assign them roper class. This is based on user’s understand of the study area and visual inspection of the image.

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ERDAS Imagine: Classification using Minimum Distance Classifier

ERDAS Imagine: Classification using Minimum Distance Classifier

This video demonstrates how to perform image classification using Minimum Distance classifier in ERDAS Imagine. The minimum distance classifier (MDC) is an example of a commonly used ‘conventional’ classifier. The minimum distance classifier is used to classify unknown image data to classes which minimize the distance between the image data and the class in multi-feature space. The distance is defined ...

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ERDAS Imagine: Classification using Maximum likelihood Classifier

ERDAS Imagine: Classification using Maximum likelihood Classifier

This video demonstrates how to perform image classification using Maximum likelihood Classifier in ERDAS Imagine. In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a statistical model given observations, by finding the parameter values that maximize the likelihood of making the observations given the parameters. MLE can be seen as a special case of the ...

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