Multispectral image classification using rough set theory and the comparison with parallelepiped classifier
Chih‐Cheng Hung, Hendri Purnawan, Bor‐Chen Kuo · 2007
This paper explores the effectiveness of the rough set theory in multispectral image classification. A new multispectral image classification approach is proposed based on the rough set theory which uses upper and lower bounds for the class description. Rough set theory is used for classification rules extraction. A comparison of this method with the parallelepiped classifier, where the former uses the concept of cuts and the later uses the maximum and minimum values, is compared. Preliminary experimental results show that the proposed classifier is effective for multispectral image classification.