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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/3255

Title: Designing optimal sampling schemes for field visits
Authors: Debba, P
Keywords: Remote sensing images
Optimal filed sampling schemes
Hyperspectral image
Designing
Spectral angle mapper
Spectral feature fitting
SFF
Issue Date: Oct-2008
Publisher: CSIR
Citation: Debba, P. 2008. Designing optimal sampling schemes for field visits, (Presentation excluding conference presentations), CSIR Natural Resources and the Environment, Weekly seminar series in Ecosystem Processes and Dynamics, Tea Room in Building 4E, pp 38
Abstract: This is a presentation of a statistical method for deriving optimal spatial sampling schemes. The research focuses on ground verification of minerals derived from hyperspectral data. Spectral angle mapper (SAM) and spectral feature fitting (SFF) classification techniques were applied to obtain rule mineral images. Each pixel in these rule images represents the similarity between the corresponding pixel in the hyperspectral image to a reference spectrum. The rule images provide weights that are utilized in objective functions of the sampling schemes which are optimized through a process of simulated annealing. A HyMAP 126-channel airborne hyperspectral data acquired in 2003 over the Rodalquilar area in Spain serves as an application to target those pixels with the highest likelihood of occurrence of a specific mineral and as a collection the location of these sampling points selected represent the distribution of that particular mineral. In this area, alunite being a predominant mineral in the alteration zones was chosen as the target mineral. Sampling points are distributed more intensely in regions of high probable alunite as classified by both SAM and SFF, thus representing the purest of pixels. This method leads to an efficient distribution of sample points, on the basis of a user-defined objective
Description: CSIR Built Environment Tea Room session
URI: http://hdl.handle.net/10204/3255
Appears in Collections:Logistics and quantitative methods
General science, engineering & technology

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