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Supervised: In Supervised Classification, the analyst identifies the classes by image interpretation techniques and collects signatures for making feature classes.
Unsupervised: The software tool itself classifies the image into specified number of classes by grouping nearly matching pixel values for making feature classes.
Unsupervised classification is based on pixel data of the image. Preferably raw image data is used to generate varios tags (clusters) of the image that are later used for classification.
In supervised classification the user defines/selects what the image represents and later imaging processing techniques are used to make classification
Unsupervised classification is where the outcomes (groupings of pixels with common characteristics) are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes.
Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Training sites (also known as testing sets or input classes) are selected based on the knowledge of the user. The user also sets the bounds for how similar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the training area, plus or minus a certain increment (often based on "brightness" or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into.
In supervised classification analyst identify the classes and directs the computer to classify accordingly.
Unsupervised classification makes clusters/classes based on the digital numbers or spectral properties without prior input of analyst.
