A Comparison of Parametric Classification Procedures of Remotely Sensed Data Applied on Different Landscape Units

This paper presents an evaluation of several parametric classification algorithms to assess their accuracy on various landscapes. Traditionally the maximum likelihood classifier is used to obtain thematic maps in land use. In this work different classification algorithms including contextual classifiers, one of them being original, are applied and compared on sites belonging to landscape units ranging from tiny fields surrounded by hedges to larger and more open fields. Confusion matrices and result analysis are presented at two observation scales: at the catchment area level and at the landscape unit level. We show how the choice of a classification technique can significantly influence the results of crop inventories and how the accuracy of classification algorithms vary according to the landscape units of the studied area. From these results a strategy can be developed for a better choice of classification algorithms regarding the considered landscape structure.