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Supervised Classification

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Supervised Classification
Supervised Classification is a technique for the computer-assisted interpretation of remotely sensed imagery.


un or automatic interpretation : The operation of a group of multispectral image interpretation functions (such as K-means) that statistically cluster cells into similar collections.

supervised classification develops the rules for assigning reflectance measurements to classes using a "training area", based on input from the user, then applies the rules automatically to the remaining image ...

In an un, the maximum-likelihood classifier uses the cluster means and covariance matrices from the i.

In performing a supervised classification, the representation of a single feature within an image is highly variable as a result of shadowing, terrain, moisture, atmospheric conditions, and sun angle.
Atmospheric Absorption Bands
4.

In , a sample of image elements for each land cover class is used to estimate parameters, typically a mean vector and covariance matrix, for input to the classifier.

Now, both 8-bit and 24-bit color image can be classified using R2V's power unsupervised classification function to extract and separate color classes.

S-TCC was based on un of a single multidate data set that contained the six bands from the two dates. ISODATA, an algorithm available in Imagine 8.1, was used in the un.

See also: See also: Class, Image, Classification, Map, Raster

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