Remote sensing data classification using tolerant rough set and neural networks

• Published in: Science China. Earth sciences Vol. 48; no. 12; pp. 2251 - 2259
• Main Authors: Ma, Jianwen, Hasi, Bagan
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Nature B.V 01.12.2005; State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China
• Subjects: Algorithms; Back propagation; Back propagation networks; China; Classification; Convergence; Neural networks; Preprocessing; Remote sensing; Studies; Training; China, People's Rep., Beijing; classification; neural network; BP algorithm; tolerant rough set
• ISSN: 1006-9313; 1674-7313; 1862-2801; 1869-1897
• DOI: 10.1360/03yd0514

BP algorithm of neural net is used more in remote sensing data classification. One of drawbacks of BP algorithm is the overall low function when the net is training. To avoid this kind of problem, the paper introduces the tolerant rough set for classification-preprocessing the training data to reduce the influence elements of the training convergence in order to improve the net training successful rate. ETM+data of Beijing in May 2003 is selected in the study. ETM+ data before and after classification preprocessing, respectively, are used for BP (Back propagation) training. The result shows that such a preprocessing not only compensates the drawback of BP algorithm when processing ETM+data but also improves classification accuracy.