Sampling scheme optimization from hyperspectral data

This thesis presents statistical sampling scheme optimization for geo-environmental purposes on the basis of hyperspectral data. It integrates derived products of the hyperspectral remote sensing data into individual sampling schemes. Five different issues are being dealt with. First, the optimized sampling scheme is presented to select samples that represent different ontological categories. The iterated conditional modes algorithm (ICM) is used as an unsupervised segmentation technique. Within each category, simulated annealing is applied for minimizing the mean shortest distance (MMSD) between sampling points. The number of sampling points in each category is proportional to the size and variability of the category. The combination of the ICM algorithm for image segmentation with simulated annealing for optimized sampling, results in an elegant and powerful tool in designing optimal sampling schemes using remote sensing images. A validation study conducted shows that the optimized sampling scheme gives best estimates for commonly used vegetation indices compared to simple random sampling and rectangular grid sampling. Next, optimal sampling schemes, which focus on ground verification of minerals derived from hyperspectral data, are presented. Spectral angle mapper (SAM) and spectral feature fitting (SFF) classification techniques are applied to obtain rule mineral images. The rule images provide weights that are utilized in objective functions of the sampling schemes which are optimized by means of simulated annealing. Three weight functions 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 where there is an 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. This is followed by a quantitative method for optimally locating exploration targets based on a probabilistic mineral prospectivity map, which was created by means of weights-of-evidence (WofE) modeling. Locations of discovered mineral occurrences were used as a training set and a map of distances to faults/fractures