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Texture analysis and classification of ERS SAR images for map updating of urban areas in The Netherlands / R. Dekker in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 9 (September 2003) . - pp 1950 - 1958 Titre : Texture analysis and classification of ERS SAR images for map updating of urban areas in The Netherlands Type de document : Article/Communication Auteurs : R. Dekker, Auteur Année de publication : 2003 Article en page(s) : pp 1950 - 1958 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] 1:250.000 [Termes IGN] analyse texturale [Termes IGN] cartographie urbaine [Termes IGN] classification [Termes IGN] histogramme [Termes IGN] image ERS-SAR [Termes IGN] image radar [Termes IGN] milieu urbain [Termes IGN] mise à jour cartographique [Termes IGN] Pays-Bas [Termes IGN] Rotterdam (Pays-Bas) [Termes IGN] variogramme Résumé : (Auteur) In single-band and single-polarized synthetic aperture radar (SAR) image classification, texture holds useful information. In a study to assess the map-updating capabilities of such sensors in urban areas, some modern texture measures were investigated. Among them were histogram measures, wavelet energy, fractal dimension, lacunarity, and semivariograms. The latter were chosen as an alternative for the well-known gray-level cooccurrence family of features. The area that was studied using a European Remote Sensing Satellite 1(ERS1) SAR image was the conurbation around Rotterdam and The Hague in The Netherlands. The area can be characterized as a well-planned dispersed urban area with residential areas, industry, greenhouses, pasture, arable land, and some forest. The digital map to be updated was a 1: 250 000 Vector Map (VMapl). The study was done on the basis of non-parametric separability measures and classification techniques because most texture distributions were not normal. The conclusion is that texture improves the classification accuracy. The measures that performed best were mean intensity (actually no texture), variance, weighted-rank fill ratio, and semivariogram, but the accuracies vary for different classes. Despite the improvement, the overall classification accuracy indicates that the land-cover information content of ERS1 leaves something to be desired. Numéro de notice : A2003-250 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.814628 En ligne : https://doi.org/10.1109/TGRS.2003.814628 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22545 [article]

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