<title>Soft classifications for the mapping of land cover from remotely sensed data</title>

Image classification used in mapping land cover form remotely sensed data are frequently described as being 'hard' of 'soft' yet in reality such a simple distinction is not observed and a continuum of classification softness can be defined. Using airborne sensors or imagery of two test sites in South Wales, classifications at different points along this continuum with a feedforward neural network are illustrated. It is shown that soft classification can provide a better and more accurate representation of both discrete and continuous land cover classes, resolving in particular problems associated with mixed pixels. Classifications produced at different positions along the continuum of classification softness, however, differed markedly in the representation of land cover distribution and accuracy, highlighting the need to recognize the existence of the continuum and its implications for land cover mapping from remotely sensed data. The results also highlight that the use of a soft or fuzzy classifier is only a partial solution to the mixed pixel problem; a full solution requires refinement of the training and testing stages and methods for this are discussed. Despite an ability to accommodate for the effects of mixed pixels on each of the three stages of supervised image classifications, other factors can degraded classification quality. One important issue is the presence of untrained classes. It is hon, however, that the effect of untrained classes can be reduced with the use of additional information on the typicality of class membership that can be derived form some soft classifications.