Object-oriented Remote Sensed Image Classification Accuracy Assessment
Zhaocong Wu, Lina Yi, and Guifeng Zhang
School of remote sensing and information engineering Wuhan university Wuhan, China
Abstract: This paper proposes a new accuracy assessment scheme for object-oriented remote sensing imagery classification. It measures both the geometrical and thematic accuracy of objects. The geometrical accuracy are measured from two aspects of the area and boundary accuracy. The thematic accuracy is measured associated with objects. The classification results of a Quickbird image with different object location are evaluated by the proposed method. Experiments show the method can provide more information about the classification accuracy and is potential to solve the problem of the traditional statistical accuracy assessment measures.
Keywords: object-oriented; classification accuracy assessment; segmentation quality