Maximum likelihood classification, optimal or problematic? A comparison with the nearest neighbour classification

Abstract

The maximum likelihood and the nearest neighbour classification algorithms are reviewed, particularly from the point of view of user/analyst requirements. The two algorithms were put to use for the classification or Landsat TM data of agricultural scenes and accuracy with respect to ‘ground truth’ was evaluated using different parametric settings. Results show that within the maximum likelihood classification, accuracies and errors can vary to a considerable degree depending on the formation of the statistical classes from the training data. More interestingly, it was found that the nearest neighbour algorithm produced higher accuracies and was judged to be more robust, but it has computer implementation problems with high data dimensionality.