Abstract
This paper presents a statistical method for deriving the optimal prospective field sampling scheme on a remote sensing image to represent different categories in the field. The iterated conditional modes algorithm (ICM) is used for segmentation followed by simulated annealing within each category. Derived field sampling points are more intense in heterogenous segments. This method is applied to airborne hyperspectral data from an agricultural field. The optimized sampling scheme shows superiority to simple random sampling and rectangular grid sampling in estimating common vegetation indices and is thus more representative of the whole study area. © 2008 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Debba, P., Stein, A., Van Der Meer, F. D., Carranza, E. J. M., & Lucieer, A. (2008). Field sampling from a segmented image. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5072 LNCS, pp. 756–768). https://doi.org/10.1007/978-3-540-69839-5_55
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