A spatial Poisson Point Process to classify coconut fields on Ikonos pansharpened images

Abstract : The goal of this study is to classify the coconut fields, observed on remote sensing images, according to their spatial distribution. For that purpose, we use a technique of point pattern analysis to characterize spatially a set of points. These points are obtained after a coconut trees segmentation process on Ikonos images. Coconuts' fields not following a Poisson Point Process are identified as maintained, otherwise other fields are characterized as wild. A spatial analysis is then used to establish locally the Poisson intensity and therefore to characterize the degree of wildness.
Type de document :
Communication dans un congrès
SPIE Asia Pacific Remote Sensing, Nov 2008, Nouméa, New Caledonia. 7149, pp.71491E, 2008, Proceedings of SPIE. 〈10.1117/12.806422〉
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Contributeur : Nathalie Gaudechoux <>
Soumis le : vendredi 1 avril 2011 - 12:22:32
Dernière modification le : jeudi 22 novembre 2018 - 14:27:18

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Raimana Teina, Dominique Béréziat, Benoît Stoll. A spatial Poisson Point Process to classify coconut fields on Ikonos pansharpened images. SPIE Asia Pacific Remote Sensing, Nov 2008, Nouméa, New Caledonia. 7149, pp.71491E, 2008, Proceedings of SPIE. 〈10.1117/12.806422〉. 〈inria-00582390〉

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