ABSTRACT
The estimation of the accuracy of a thematic classification derived from remotely sensed data is generally based on the confusion or error matrix. This matrix is derived by evaluating the performance of the classifier on a set of test data. The quantities derived from analysis of the confusion matrix include percent accuracy (total and per class), producer’s accuracy, consumer’s accuracy, and varieties of the kappa coefficient. None of these measures considers the spatial pattern of erroneously classified pixels, either implicitly or explicitly. Furthermore, each pixel in the image is assigned a unique (“hard”) label, so that it is not possible to ascertain the reliability of the classification. In this paper we propose a methodology that specifically takes into account the spatial pattern of errors of omission and commission, and which presents the user with an indication of the reliability of pixel label assignments. Our methodology assumes that an accurate digital map of the spatial objects (fields, lakes, or forests) being classified, plus cultural features such as roads and urban areas are available. Such a map can be obtained by digitising a large-scale paper map of the study area, or via image processing.
