High resolution remote sensing image classification with multiple classifiers based on mixed combining rule

Zhong Chen; Jianguo Liu; Guoyou Wang

doi:10.1117/12.749811

15 November 2007 High resolution remote sensing image classification with multiple classifiers based on mixed combining rule

Zhong Chen, Jianguo Liu, Guoyou Wang

Author Affiliations +

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67881J (2007) https://doi.org/10.1117/12.749811
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

The development of the remote sensing technology makes us obtain very abundant information of nature, especially with the appearance of high resolution remote sensing image it extends the visual field of the nature. High-resolution satellite images such as Quickbird and IKONOS have been applied into many fields. But the challenge that faces us is how to make use of the data effectively and obtain more useful information through some processing. Because in the target recognition, the mutual-complementarity among the different results obtained by the different classifier making using of the same features usually is very strong and high resolution remote sensing data have a lot of characteristics such as spectral, texture and context and so on compared to the other lower resolution remote sensing data, the Multiple Classifiers making use of multi-characteristic was proposed to improve the high resolution remote sensing image classification accuracy in this paper. The experiments show that the approach can obtain higher classification accuracy and better classification result than single classifier.

Citation Download Citation

Zhong Chen, Jianguo Liu, and Guoyou Wang "High resolution remote sensing image classification with multiple classifiers based on mixed combining rule", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67881J (15 November 2007); https://doi.org/10.1117/12.749811

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