Rough set-derived measures in image classification accuracy assessment
Yong Ge, Hexiang Bai, Feng Cao, Sanping Li, Xianfeng Feng, Deyu Li · International Journal of Remote Sensing · 2009
Currently, there are two types of measure in image classification accuracy assessment: pixel-level measures and category-level/map-level measures. These have their own limitations for representing the uncertainty at pixel and category/map levels. In addition, some of these measures derived from the error matrix are obtained by collecting reference data and then they may be affected by factors related to the sampling. This paper uses rough set theory to obtain the rough degree, rough entropy, quality of approximation and accuracy of approximation. Incorporating traditional measures, they compose one kind of three-level architecture for the classified image, which contains pixel-level measures, object/category-level measures and map-level measures. Unlike some conventional measures, these new measures can be derived directly from the supervised classification result without collecting reference data. A case study on the Landsat TM image is used to substantiate the conceptual arguments. The results demonstrate that the proposed measures are valid for measuring the accuracy of classified remotely sensed imagery and can provide additional information to conventional measures.