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. 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. Three weight functions are defined to intensively sample areas where a high probability and abundance of alunite occurs. Weight function I uses binary weights derived from the SAM classification image, leading to an even distribution of sampling points over the region of interest. Weight function II uses scaled weights derived from the SAM rule image. Sample points are arranged more intensely in areas of abundance of alunite. Weight function III combines information from several different rule image classifications. 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.