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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.

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
unsupervised classification develops the rules automatically ...

In an unsupervised classification, the maximum-likelihood classifier uses the cluster means and covariance matrices from the i.cluster signature file to determine to which category (spectral class) each cell in the image has the highest probability of belonging.

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. Photogrammetry ...

In supervised classification, 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 un~ function to extract and separate color classes. Both center line and boundary line of color classes can be vectorized automatically using R2V's vectorization function.

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~. Fifty unsupervised signatures were extracted.

See also: See also: What is the meaning of Image, Classification, Class, Imagery, Raster?

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