Centre de documentation
scientifique

Large-area land-cover mapping through scene-based classification compositing / B. Guindon in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 6 (June 2002)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 68 n° 6 (June 2002) . - pp 589 - 596 Titre : Large-area land-cover mapping through scene-based classification compositing Type de document : Article/Communication Auteurs : B. Guindon, Auteur ; C.M. Edmonds, Auteur Année de publication : 2002 Article en page(s) : pp 589 - 596 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] bassin hydrographique [Termes IGN] carte d'occupation du sol [Termes IGN] classification non dirigée [Termes IGN] image Landsat-MSS [Termes IGN] image Landsat-TM [Termes IGN] modèle stéréoscopique [Termes IGN] recouvrement d'images Résumé : (Auteur) 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 ]andcover 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. Numéro de notice : A2002-132 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans En ligne : https://www.asprs.org/wp-content/uploads/pers/2002journal/june/2002_jun_589-596. [...] Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22047 [article]