The purpose of this study is to classify the types of coconut plantation. To this end, we compare several classifiers
such as Maximum Likelihood, Minimum Distance, Parallelepiped, Mahalanobis and Support Vector Machines
(SVM). The contribution of textural informations and spectral informations increases the separability of different
classes and then increases the performance of classification algorithms. Before comparing these algorithms, the
optimal windows size, on which the textural information are computed, as well as the SVM parameters are first
estimated. Following this study, we conclude that SVM gives very satisfactory results for coconut field type
mapping.
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.
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