Supervised Classification is a technique for the computer-assisted interpretation of remotely sensed imagery.
Supervised classification is much more accurate for mapping classes, but depends heavily on the cognition and skills of the image specialist.
A type of automatic multi-spectral image interpretation in which the user supervises feature classification by setting up prototypes (collections of sample points) for each feature, class, or land cover to be mapped.
unsupervised classification or automatic interpretation : The operation of a group of multispectral image interpretation functions (such as K-means) that statistically cluster cells into similar collections.
unsupervised classification: The grouping of pixels by their numerical spectral characteristics without the intervention of direct human guidance.
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 unsupervised classification, 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
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 unsupervised classification function to extract and separate color classes.
S-TCC was based on unsupervised classification 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 unsupervised classification.
See also: Class, Image, Classification, Map, Raster