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 heterogeneous 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.
Reference:
Debba P et al. 2008. Field Sampling from a Segmented Image. Computational science and its applications (ICCSA 2008) International Conference, Perugia, Italy, June 30 - July 3, 2008; Part I, pp 756-768
Debba, P., Stein, A., Van der Meer, F., Carranza, E., & Lucieer, A. (2008). Field Sampling from a Segmented Image. http://hdl.handle.net/10204/2334
Debba, Pravesh, A Stein, FD Van der Meer, EJM Carranza, and A Lucieer "Field Sampling from a Segmented Image." (2008) http://hdl.handle.net/10204/2334
Debba P, Stein A, Van der Meer F, Carranza E, Lucieer A. Field Sampling from a Segmented Image. 2008; http://hdl.handle.net/10204/2334.
