Quantifying Uncertainty in Land-Use/Land-Cover Classification Accuracy: A Stochastic Simulation Approach

Abstract

In numerous applications of land-use/land-cover (LULC) classification, the classification rules are determined using a set of training data; thus, the results are inherently affected by uncertainty in the selection of those data. Few studies have assessed the accuracy of LULC classification with this consideration. In this article, we provide a general expression of various measures of classification accuracy with regard to the sample data set for classifier training and the sample data set for the evaluation of the classification results. We conducted stochastic simulations for LULC classification of a two-feature two-class case and a three-feature four-class case to show the uncertainties in the training sample and reference sample confusion matrices. A bootstrap simulation approach for establishing the 95% confidence interval of the classifier global accuracy was proposed and validated through rigorous stochastic simulation. Moreover, theoretical relationships among the producer accuracy, user accuracy, and overall accuracy were derived. The results demonstrate that the sample size of class-specific training data and the a priori probabilities of individual LULC classes must be jointly considered to ensure the correct determination of LULC classification accuracy.

Multi-Temporal Passive and Active Remote Sensing for Agricultural Mapping and Acreage Estimation in Context of Small Farm Holds in Ethiopia

“…This disparity can be attributed to the error matrix, which indicates that some of the proportion of the area of agricultural land is omitted from the map. These results are in line with other similar studies that have also demonstrated the improved accuracy of area estimation by considering the uncertainty in the error matrix and confidence interval [198][199][200].…”

Finding complement of inefficient feature clusters obtained by metaheuristic optimization algorithms to detect rock mineral types

“…Since the confusion matrix is preferred in measurement analysis in many study analyses, it was also used in this study. The metrics that make up the confusion matrix are as follows: accuracy (Acc), sensitivity (Se), specificity (Sp), precision (Pre), and F -score ( F -Scr) (Cheng et al, 2021; Polat et al, 2020). The following equations are used to calculate the metrics.…”