Weights Derived from Hyperspectral Data to Facilitate an Optimal Field Sampling Scheme for Potential Minerals

This paper presents a statistical method for deriving optimal spatial sampling schemes. It 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. The rule images provide weights that are utilized in simulated annealing. A HyMAP 126channel 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. A weight function is defined to intensively sample areas where a high probability and abundance of alunite occurs. This method leads to an efficient distribution of sample points, on the basis of a user-defined objective.