Large-area land-cover mapping through scene-based classification compositing

Over the past decade, a number of initiatives have been undertaken to create definitive national and global data sets consisting of precision corrected Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM) scenes. One important application of these data is the derivation of large area land-cover products spanning multiple satellite scenes. A popular approach to land-cover mapping on this scale involves merging constituent scenes into image mosaics prior to image clustering and cluster labeling, thereby eliminating redundant geographic coverage arising from overlapping imaging swaths of adjacent orbital tracks. In this paper, arguments are presented to support the view that areas of overlapping coverage contain important information that can be used to assess and improve classification performance. A methodology is presented for the creation of large area land-cover products through the compositing of independently classified scenes. Statistical analyses of classification consistency between scenes in overlapping regions are employed both to identify mislabeled clusters and to provide a measure of classification confidence for each scene at the cluster level. During classification compositing, confidence measures are used to rationalize conflicting classifications in overlap regions and to create a relative confidence layer, sampled at the pixel level, which characterizes the spatial variation in classification quality over the final product. The procedure is illustrated with results from a synoptic mapping project of the Great Lakes watershed that involved the classification and compositing of 46 Landsat MSS scenes.