Field Sampling from a Segmented Image

Authors:
Pravesh Debba;Alfred Stein;Freek D. Meer;Emmanuel John Carranza;Arko Lucieer
Affiliations:
Council for Scientific and Industrial Research (CSIR), Logistics and Quantitative Methods, CSIR Built Environment, South Africa 0001 and College of Science, Engineering and Technology, Department ...;International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, The Netherlands 7500AA;International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, The Netherlands 7500AA;International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, The Netherlands 7500AA;School of Geography & Environmental Studies, Center for Spatial Information Science (CenSIS), University of Tasmania, Tasmania, Australia 7001
Venue:
ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
Year:
2008

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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.