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

R. Teina; D. Béréziat; B. Stoll

doi:10.1117/12.806422

11 December 2008 A spatial Poisson Point Process to classify coconut fields on Ikonos pansharpened images

R. Teina, D. Béréziat, B. Stoll

Proceedings Volume 7149, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications II; 71491E (2008) https://doi.org/10.1117/12.806422
Event: SPIE Asia-Pacific Remote Sensing, 2008, Noumea, New Caledonia

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.

Citation Download Citation

R. Teina, D. Béréziat, and B. Stoll "A spatial Poisson Point Process to classify coconut fields on Ikonos pansharpened images", Proc. SPIE 7149, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications II, 71491E (11 December 2008); https://doi.org/10.1117/12.806422

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