Assessing Uncertainty in LULC Classification Accuracy by Using Bootstrap Resampling

Assessing Uncertainty in LULC Classification Accuracy by Using Bootstrap Resampling

Supervised land-use/land-cover (LULC) classifications are typically conducted using class assignment rules derived from a set of multiclass training samples. Consequently, classification accuracy varies with the training data set and is thus associated with uncertainty. In this study, we propose a bootstrap resampling and reclassification approach that can be applied for assessing not only the ...

Improving Classification Accuracy Assessments with Statistical Bootstrap Resampling Techniques

The use of remotely sensed imagery to generate land cover models is common today. Validation of these models typically involves the use of an independent set of ground-truth data which are used to calculate an error matrix resulting in estimates of omission, commission, and overall error. However, each estimate of error contains a degree of uncertainty itself due to 1) conceptual bias, 2) locat...

Assessing Sampling Uncertainty in FVS Projections Using a Bootstrap Resampling Method

USDA Forest Service Proceedings RMRS-P-25. 2002 In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO. Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. T. F. Gregg recently retired from the USDA Forest Service, Region 6 in Portland, OR. He was the...

Efficient Bootstrap Resampling

Bootstrap principle is briefly reviewed. Hall’s (1989) antithetic variates method for bootstrap is discussed and extended to more than two antithetic resampling processes. We illustrate the theory with a simulation study. The numerical results show that increasing the number of antithetic resampling processes produces significant smaller variances of the bootstrap estimator over the paired case.

- multiscale bootstrap resampling