Uncertainty Comparison for Area-Class Maps Concerning Different Reference Data

Abstract

As the main factor that influences classification quality, uncertainty characterization is analysis of area classes based on remotely sensed imagery and auxiliary data. This study focuses on uncertainty comparison between reference and classification maps. By referring to information classes and data classes respectively, experiment using real data sets was carried out to quantify uncertainty in area-class maps. Contingency tables and an information theory measure of shared information, percentage of average mutual information (%AMI), were applied to compare the uncertainty between pairs of area-class maps, where maximum likelihood classifier was used to classify the image into area-class map with different reference data on the discriminant space. Results show that there exist large impacts of semantic bias in different reference on classification uncertainty. Therefore, further improving upon the effect of bias in reference data will be studied to enable a more accurate assessment of the quality of classification.