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High spatial resolution satellite imagery, DEM derivatives, and image segmentation for the detection of mass wasting processes / J. Barlow in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 6 (June 2006)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 72 n° 6 (June 2006) . - pp 687 - 692 Titre : High spatial resolution satellite imagery, DEM derivatives, and image segmentation for the detection of mass wasting processes Type de document : Article/Communication Auteurs : J. Barlow, Auteur ; Steven E. Franklin, Auteur ; Y. Martin, Auteur Année de publication : 2006 Article en page(s) : pp 687 - 692 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] classification ascendante hiérarchique [Termes IGN] classification dirigée [Termes IGN] effondrement de terrain [Termes IGN] géomorphologie locale [Termes IGN] image à haute résolution [Termes IGN] image SPOT 5 [Termes IGN] interprétation automatique [Termes IGN] modèle numérique de surface [Termes IGN] réflectance du sol [Termes IGN] relief [Termes IGN] segmentation d'image Résumé : (Auteur) An automated approach to identifying landslides using a combination of high-resolution satellite imagery and digital elevation derivatives is offered as an alternative to aerial photographic interpretation. Previous research has demonstrated that per pixel spectral response patterns are ineffective in discriminating mass movements. This technique utilizes image segmentation and digital elevation data in order to identify mass movements based not only on their reflectance but also on their shape properties and their geomorphic context. Dividing the classification by process into debris slides, debris flows, and rock slides makes the method far more useful than methods that group all mass movements together. A hierarchical classification scheme is utilized to eliminate areas that are not of interest and to identify areas where mass movements are probable. A supervised classification is then conducted using spectral, shape, and textural properties to identify failures that were greater than 1 ha in area. The resulting accuracy was 90 percent for debris slides, 60 percent for debris flows, and 80 percent for rock slides. Copyright ASPRS Numéro de notice : A2006-232 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.72.6.687 En ligne : https://doi.org/10.14358/PERS.72.6.687 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27959 [article]

Mapping built-up areas from multitemporal interferometric SAR images: a segment-based approach / Leena Matikainen in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 6 (June 2006)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 72 n° 6 (June 2006) . - pp 701 - 714 Titre : Mapping built-up areas from multitemporal interferometric SAR images: a segment-based approach Type de document : Article/Communication Auteurs : Leena Matikainen, Auteur ; M.E. Engdahl, Auteur ; Juha Hyyppä, Auteur Année de publication : 2006 Article en page(s) : pp 701 - 714 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] cartographie automatique [Termes IGN] classification dirigée [Termes IGN] densité du bâti [Termes IGN] image ERS-SAR [Termes IGN] image radar moirée [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] interprétation automatique [Termes IGN] milieu urbain [Termes IGN] segmentation d'image [Termes IGN] utilisation du sol Résumé : (Auteur) Automatic mapping of built-up areas from a multitemporal interferometric ERS-1/2 Tandem dataset was studied. The image data were segmented into homogeneous regions, and the regions were classified as built-up areas, forests, and open areas using their mean intensity and coherence values and additional contextual information. Compared with a set of reference points, an overall classification accuracy of 97 percent was achieved. The classification process was highly automatic and resulted in homogeneous regions resembling a map drawn by a human interpreter. The feasibility of the imagery for dividing built-up areas further into subclasses was also investigated. The results suggest that low-rise areas, high-rise areas, and industrial areas are difficult to distinguish from each other. On the other hand, a correlation between the building density, the proportion of land covered with buildings, and intensity/coherence in the image data was found. The dataset thus appeared to be promising for classifying built-up areas into subclasses according to building density. Copyright ASPRS Numéro de notice : A2006-233 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.72.6.701 En ligne : https://doi.org/10.14358/PERS.72.6.701 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27960 [article]