Traditional classification accuracy assessments based on summary statistics from a confusion matrix furnish a global (location invariant) view of classification accuracy. To estimate the spatial distribution of classification accuracy, a geostatistical integration approach is presented in this paper. Indicator kriging with local means is combined with logistic regression to integrate an image-d...

Following an accidental release of radioactivity to the environment decisions will be made based on the information available to those organisations responsible for assessment of the radiological impact of the accident. This paper considers the application of the method of median indicator kriging to post-accident deposition data, in order to produce results in a form suitable for use by decisi...

Operating complexity, especially in the determination of cut-off values and the associated spatial variation, has been a long time standing problem for indicator kriging (IK) mineral reserves estimation method. In order to solve this problem, this text proposes an alternative method to implement IK reserves estimation in computer software. In this solution, threshold values of IK are dynamicall...

This paper develops a simulation optimization algorithm based on Taylor Kriging and evolutionary algorithm (SOAKEA) for simulation models with high computational expenses. In SOAKEA, an evolutionary algorithm is used to search for optimal solutions of a simulation model, and Taylor Kriging temporarily serves as a surrogate fitness function of this evolutionary algorithm to evaluate solutions. T...

Sequential simulation of a continuous variable usually requires its transformation into a binary or a Gaussian variable, giving rise to the classical algorithms of sequential indicator simulation or sequential Gaussian simulation. Journel (1994) showed that the sequential simulation of a continuous variable, without any prior transformation, succeeded in reproducing the covariance model, provid...

Sequential Gaussian simulation (sgsim) and sequential indicator simulation (sisim) have emerged as powerful tools for stochastic imaging of Earth Science phenomena and are currently widely accepted fast simulation algorithms. In this paper, we will expand the sequential simulation toolbox by relying on a recent development in geostatistical theory (Journel, 1994) denoted as direct sequential si...

In spatial data modelling and analysis there are a variety of techniques to perform prediction. The goal of these techniques is to take spatially located data and to establish estimates of data values at unknown locations. Of these techniques, the attractive aspects of kriging are often overshadowed by the slow speed of the calculation. Unfortunately the calculations necessary to perform krigin...

Image inpainting is the art of predicting damaged regions of an image. The manual way of image inpainting is a time consuming. Therefore, there must be an automatic digital method for image inpainting that recovers the image from the damaged regions. In this paper, a novel statistical image inpainting algorithm based on Kriging interpolation technique was proposed. Kriging technique automatical...

In this paper, we perform an experimental study to investigate directional variograms in punctual kriging and consequently its effect on image restoration. We employ punctual kriging in conjunction with fuzzy logic typeII and fuzzy smoothing based approaches to remove white Gaussian noise from corrupted images. Images degraded with Gaussian white noise are restored by first utilizing fuzzy logi...

We report an intelligent image restoration approach by combining the geostatistical interpolation technique of punctual kriging and the machine learning approach of adaptive learning. Digital images degraded from Gaussian white noise are restored by first utilizing fuzzy logic for selecting pixels that need to be kriged. The concept of punctual kriging is then used to estimate the intensity of ...