Spatial Estimation of Classification Accuracy Using Indicator Kriging with an Image-Derived Ambiguity Index

Spatial Estimation of Classification Accuracy Using Indicator Kriging with an Image-Derived Ambiguity Index

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...

Mapping Spatial Thematic Accuracy Using Indicator Kriging

Image Thresholding by Indicator Kriging

ÐWe consider the problem of segmenting a digitized image consisting of two univariate populations. Assume a priori knowledge allows incomplete assignment of voxels in the image, in the sense that a fraction of the voxels can be identified as belonging to population 0, a second fraction to 1, and the remaining fraction have no a priori identification. Based upon estimates of the short length sca...

Truncated Gaussian Kriging as an Alternative to Indicator Kriging

Truncated Gaussian simulation (TGS) and plurigaussian simulation (PGS) are widely accepted methods for generating realisations of geological domains (lithofacies) that reproduce contact relationships. The realisations can be used to evaluate transfer functions related to the lithofacies occurrence, the simplest ones of which are the probability of occurrence of each lithofacies and the most pro...

Spatial Estimation of Soil Moisture and Salinity with Neural Kriging

The study was carried out with 107 measurements of volumetric soil water content (SWC) and electrical conductivity (EC) for soil profile (0-30 cm) and the estimating accuracy of ordinary kriging (OK) and back-propagation neural network (BPNN) was compared. The results showed that BPNN method predicted a slightly better accurate SWC than that of OK, but differences between both methods were not ...