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Classifying land development in high-resolution panchromatic satellite images using straight-line statistics / C. Unsalan in IEEE Transactions on geoscience and remote sensing, vol 42 n° 4 (April 2004)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 42 n° 4 (April 2004) . - pp 907 - 919 Titre : Classifying land development in high-resolution panchromatic satellite images using straight-line statistics Type de document : Article/Communication Auteurs : C. Unsalan, Auteur ; K.L. Boyer, Auteur Année de publication : 2004 Article en page(s) : pp 907 - 919 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] aménagement du territoire [Termes IGN] classificateur non paramétrique [Termes IGN] classificateur paramétrique [Termes IGN] détection de contours [Termes IGN] image à haute résolution [Termes IGN] image panchromatique [Termes IGN] méthode robuste [Termes IGN] objet géographique linéaire [Termes IGN] périphérie urbaine [Termes IGN] zone rurale [Termes IGN] zone urbaine Résumé : (Auteur) We introduce a set of measures based on straight lines to assess land development levels in high-resolution (1 m) panchromatic satellite images. Most urban areas locally (such as in a 400 x 400 M2 area) exhibit a preponderance of straight-line features, generally appearing in fairly simple quasi-periodic organizations. Wilderness and rural areas produce line structures in more random spatial arrangements. We use this observation to perform an initial triage on the image to restrict the attention of subsequent more computationally intensive analyses. Statistical measures based on straight lines guide the analysis. We base these measures on length, contrast, orientation, periodicity, and location. On these, we trained and tested parametric and nonparametric classifiers. These tests were for a two-class problem (urban versus rural). However, because our ultimate goal is to extract residential regions, we then extended these ideas to address the detection of suburban regions. To do so, some use of spatial coherence is required; suburban regions are especially difficult to detect. Therefore, we introduce a decision system to perform suburban region classification via an overlapping voting method for consensus discovery. Our data were taken from regions all around the world, which underscores the robustness of our approach. Based on extensive testing, we can report very promising results in distinguishing developed areas. Numéro de notice : A2004-188 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.818835 En ligne : https://doi.org/10.1109/TGRS.2003.818835 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26715 [article]

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