Image is continuous data. To obtain the variation within the continuous data similar to discrete data, image classification data is important. Image are classified in the classes as required. Terms used: Feature Extraction, Pattern Recognition *Spectral* (pixel based on its pattern of radiance measurements) and *Spatial* (pixels classified related to its neighboring pixels, complex) *Temporal* (change in pixels over time) Types of Classification: Unsupervised: Aggregation based on spectral clusters; knowledge of thematic classes none, The machine does all the work; popular but limitations remain Interpretation based on the spectral relations, Human analyst determines their usability (renames the clusters) and may adjust clustering parameters, Advantages: Missed training sites in supervised gets chance to be visible and assumptions not required Supervised: Training sets and areas are provided; based on "looks alike"
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