Supervised Training and Classification

Selection of Training Areas

DN’s of training fields plotted on a “scatter” diagram in two-dimensional feature space Band 2 Band 1 from Lillesand & Kiefer

Classification Algorithms/Decision Rules Non-parametric decision rule – independent of the properties/statistics of the data – does not assume normal distribution – Example: Parallelepiped Parametric decision rule – assumes normal distribution – defined by the signature mean vector and covariance matrix – Examples: Minimum distance, Mahalanobis distance, Maximum likelihood

Parallelepiped “In the parallelepiped decision rule, the data file values of the candidate pixel are compared to upper and lower limits” the minimum and maximum data file values of each band in the signature - the mean of each band, plus and minus a number of standard deviations

Parallelepiped Fastest of all classifiers Problem of “overlapping bounds” Problem of “corner pixels”

Minimum Distance “Calculates the spectral distance between the measurement vector for the candidate pixel and the mean vector for each signature.” Advantages No unclassified pixels Disadvantages Does not incorporate variation Pixels that should be unclassified become classified

Maximum Likelihood For each pixel to be classified: The probability of classification is calculated for each class The pixel is classified as the class with the largest probability The slowest of classifiers discussed Theoretically the best classification

Maximum Likelihood Degrees of Probability

Maximum Likelihood

Maximum Likelihood Water Forest Urban 0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 60 70 80 90 Band 4 - DN