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Automatic detection and segmentation of orchards using very high resolution imagery / Selim Aksoy in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 8 (August 2012) . - pp 3117 - 3131 Titre : Automatic detection and segmentation of orchards using very high resolution imagery Type de document : Article/Communication Auteurs : Selim Aksoy, Auteur ; I. Yalniz, Auteur ; K. Tasdemir, Auteur Année de publication : 2012 Article en page(s) : pp 3117 - 3131 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse texturale [Termes IGN] détection automatique [Termes IGN] image à très haute résolution [Termes IGN] image Ikonos [Termes IGN] image optique [Termes IGN] image Quickbird [Termes IGN] segmentation d'image [Termes IGN] Turquie [Termes IGN] verger Résumé : (Auteur) Spectral information alone is often not sufficient to distinguish certain terrain classes such as permanent crops like orchards, vineyards, and olive groves from other types of vegetation. However, instances of these classes possess distinctive spatial structures that can be observable in detail in very high spatial resolution images. This paper proposes a novel unsupervised algorithm for the detection and segmentation of orchards. The detection step uses a texture model that is based on the idea that textures are made up of primitives (trees) appearing in a near-regular repetitive arrangement (planting patterns). The algorithm starts with the enhancement of potential tree locations by using multi-granularity isotropic filters. Then, the regularity of the planting patterns is quantified using projection profiles of the filter responses at multiple orientations. The result is a regularity score at each pixel for each granularity and orientation. Finally, the segmentation step iteratively merges neighboring pixels and regions belonging to similar planting patterns according to the similarities of their regularity scores and obtains the boundaries of individual orchards along with estimates of their granularities and orientations. Extensive experiments using Ikonos and QuickBird imagery as well as images taken from Google Earth show that the proposed algorithm provides good localization of the target objects even when no sharp boundaries exist in the image data. Numéro de notice : A2012-385 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2180912 Date de publication en ligne : 31/01/2012 En ligne : https://doi.org/10.1109/TGRS.2011.2180912 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31831 [article]

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