Object-oriented remote sensing imagery classification accuracy assessment based on confusion matrix

Abstract

This paper designs an object-based confusion matrix (OCM) classification accuracy assessment scheme to accurately estimate the overall and individual category classification accuracy. The estimation protocol and the sample data collection procedures are both taken into account. On the one hand, the two commonly used OCM construction methods based on object element and weighted by object area are analyzed, which indicate the classifier's distinguish ability and the thematic map accuracy respectively. With consideration that the object location uncertainty introduces the reference category uncertainty and may lead to the bias of thematic map accuracy assessment result, a novel fuzzy OCM construction method is proposed to more accurately assess the classification accuracy. On the other hand, a simple sample data collection strategy is proposed and validated to collect representative accuracy assessment samples. The object-oriented Quickbird image land use classification accuracy assessment experiment results are analyzed to validate the applicability of the proposed schemes. Suggestions on how to use object-based confusion matrix method in classification accuracy assessment are given in conclusion.