Estimation of sub-pixel land cover composition in the presence of untrained classes

Abstract

Remotely sensed data are an attractive source of land cover data over a wide range of spatial and temporal scales. The realisation of the full potential of remote sensing as a source of land cover data is, however, restricted by numerous factors. One commonly encountered problem is the presence of mixed pixels, which cannot be appropriately accommodated in conventional image classification techniques used in thematic mapping from remotely sensed data. This problem has generally been resolved through the adoption of a soft or fuzzy classification from which the fractional coverage of classes in the image pixels may be mapped. In this type of approach, the strength of membership, a pixel displays to a class, is used as a surrogate for the fractional coverage of that class. The accuracy of the resulting land cover representation is, therefore, dependent on the relationships between class membership strength and associated class fractional coverage. Since class membership can only be measured in relation to the classes defined in the training stage of the classification, untrained classes may influence the accuracy of the class composition estimation. For example, a pixel representing an area of an untrained class can only display membership to the trained classes. The effect of an untrained class on the accuracy of sub-pixel class composition estimation will depend on how the class membership strength is calculated. Here, the effect of untrained classes on sub-pixel land cover composition estimation using algorithms that produce relative and absolute measures of class membership was assessed. The algorithms investigated were the widely used fuzzy c-means (FCM) and its possibilistic counterpart, the possibilistic c-means (PCM), algorithms which derive relative and absolute measures of class membership strength, respectively. Both algorithms were able to provide accurate estimates of sub-pixel land cover composition. When all classes had been defined in training a classification, the FCM generally provided the most accurate class composition estimates. The presence of an untrained class, however, could substantially degrade the accuracy of the sub-pixel land cover composition estimates derived from the FCM but had no effect on those from the PCM. Since untrained classes are commonly encountered it may be more appropriate to use approaches such as the PCM in addition to, or instead of, the FCM to enhance the extraction of land cover information from remotely sensed data.