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TitleLarge area Land Cover Mapping Through Scene-Based Classification Compositing
DownloadDownloads (Preprint)
LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorGuindon, B; Edmonds, C M
SourcePhotogrammetric Engineering and Remote Sensing vol. 68, no. 6, 2002 p. 589-596, Open Access logo Open Access
Alt SeriesEarth Sciences Sector, Contribution Series 20043042
PublisherAmerican Society for Photgrammetry and Remote Sensing
Mediapaper; on-line; digital
File formatpdf
NTS40J; 40I; 40O; 40P; 41G; 41H; 41A; 41B
AreaGreat Lakes
Lat/Long WENS -92.0000 -78.0000 49.0000 40.5000
Subjectsremote sensing; mapping techniques; LANDSAT; satellite imagery; Multispectral Scanner (MSS); Thematic Mapper (TM); Classification
Illustrationssatellite images; graphs; tables; flow charts
Released2002 01 01
AbstractOver 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 landcover 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.

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