利用改进Fisher分类器进行遥感图像变化检测

doi:10.3969/j.issn.1001-2400.2012.05.003

Abstract

Abstract:

This paper proposes a novel change detection technique, which treats the detection problem as a classifier problem and uses the improved dynamic Fisher classifier to identify the changes in the joint intensity histogram. By considering the relationship between the pixel and its neighborhood, local mean dynamic Fisher discriminant analysis (LMDFDA) is proposed to introduce the neighborhood’s information. Meanwhile, the parameters of the classifier are adjusted according to the current detection result, which avoids the influences of initial conditions. The proposed method is distribution free, context-sensitive and not affected by comparison operators. Experiments show that the proposed algorithm is effective and feasible for real multi-temporal remote sensing images.

Key words: change detection, distribution free, dynamic Fisher classifier, mean shift

XIN Fangfang;JIAO Licheng;WANG Lingxia;WANG Guiting. Change detection in multitemporal remote sensing images based on local mean dynamic Fisher discriminant analysis[J].J4, 2012, 39(5): 12-17+29.

References

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Change detection in multitemporal remote sensing images based on local mean dynamic Fisher discriminant analysis

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