The Maximum Likelihood Classification

This classification assigns each pixel having feature x to the class c whose units are most probable to have given rise to feature vector x. It assumes that training data statistics for each class in each band are Gaussian in nature (Uni-modal). 

Bi-modal or tri-modal histograms in a single band are not ideal for max-like classification, in such cases, individual modes probably represent individual classes that should be trained upon individually and labeled as separate classes, thus producing unimodal data. 

Maximum likelihood classification makes use of the mean measurement vector, M, for each class and covariance matrix for class c for bands k through i, V. p Pf, where i= 1,2,3. m possible classes 

To classify the measurement vector v of an unknown pixel into a class, the maximum likelihood decision rule computes the value p for each class. Then it assigns the pixel to the class that has the maximum value. 

In this image 16 classes are identified. These classes are identical to the previous ones recorded in the minimum distance image. In both instances, the Sediment class has been subdivided into three levels and two Urban classes are attempted, to account for visual differences between them (Fig. 18). 

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The accuracy assessment of the two classifications shows that using the proposed methodology has led to a significant increase in classification accuracies. The accuracy achieved for the maximum likelihood classification is 67 and 79% in urban and peri-urban areas, respectively. 

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