DocumentCode
476302
Title
Modeling multisource remote sensing image classifier based on the MDL principle: Theoretical aspects
Author
Yin, Qian ; Guo, Ping ; Yuan, Zhi-Yong ; Wei, Zu-kuan ; Zeng, Wen-yi
Author_Institution
Image Process. & Pattern Recognition Lab., Beijing Normal Univ., Beijing
Volume
6
fYear
2008
fDate
12-15 July 2008
Firstpage
3497
Lastpage
3502
Abstract
A theoretical study for modeling technique of the remote sensing image classification based on the minimum description length (MDL) principle is presented in the paper. According to the MDL principle, modeling problem is an optimization procedure to find the shortest expected code length. Kullback-Leibler (KL) divergence is adopted as the system cost function to measure expected codelength, and the codelength will be the model we desired. The advantage of using the MDL principle to build appropriate model is analyzed theoretically, model optimization technique also is described.
Keywords
geophysical signal processing; image classification; remote sensing; Kullback-Leibler divergence; MDL principle; minimum description length; multisource remote sensing image classifier; optimization technique; shortest expected code length; Atmospheric modeling; Context modeling; Cybernetics; Feature extraction; Image classification; Image recognition; Machine learning; Power system modeling; Remote monitoring; Remote sensing; Minimum Description Length; classification technique; model optimization; remote sensing image;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location
Kunming
Print_ISBN
978-1-4244-2095-7
Electronic_ISBN
978-1-4244-2096-4
Type
conf
DOI
10.1109/ICMLC.2008.4621009
Filename
4621009
Link To Document