Multi-Level Measurements for Uncertainty in Classified Remotely Sensed Imagery
Yong Ge 1+, Sanping Li 1, Ruifang Duan 1, Hexiang Bai 1 and Feng Cao 1
1Institute of Geographic Sciences and Natural Resource Research, CAS, Beijing, 100101, China
Abstract. How to measure the uncertainty in remotely sensed data is one of key issues in the uncertainty research on remotely sensed information. In this paper, we utilize information theory, rough set theory to measure the uncertainty in classified remotely sensed imagery and then propose multi-level measurement indices for classified remotely sensed imagery, that is, pixel-level index, class/object-level indices and image-level indices. Following these above discussions, a case study of the Landsat TM image on China Yellow River Delta is used to describe the multi-level measurements.
Keywords: remote sensing classification image, uncertainty, measurement indices, visual expression
In: Wan, Y. et al. (eds) Proceeding of the 8th international symposium on spatial accuracy assessment in natural resources and environmental sciences, World Academic Union (Press).