Remote Sensing Image Fusion Based on Data Assimilation and Particle Swarm Optimization

Chen Rong-yuan; Zhang Fei-yan; Zhang Bin; Qin Qian-qing

doi:10.3724/SP.J.1146.2008.01442

Volume 31 Issue 10

Dec. 2010

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Article Navigation > Journal of Electronics & Information Technology > 2009 > 31(10): 2509-2513

Chen Rong-yuan, Zhang Fei-yan, Zhang Bin, Qin Qian-qing. Remote Sensing Image Fusion Based on Data Assimilation and Particle Swarm Optimization[J]. Journal of Electronics & Information Technology, 2009, 31(10): 2509-2513. doi: 10.3724/SP.J.1146.2008.01442

Citation:

Chen Rong-yuan, Zhang Fei-yan, Zhang Bin, Qin Qian-qing. Remote Sensing Image Fusion Based on Data Assimilation and Particle Swarm Optimization[J]. Journal of Electronics & Information Technology, 2009, 31(10): 2509-2513. doi: 10.3724/SP.J.1146.2008.01442

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Remote Sensing Image Fusion Based on Data Assimilation and Particle Swarm Optimization

doi: 10.3724/SP.J.1146.2008.01442

Received Date: 2008-11-03
Rev Recd Date: 2009-03-23
Publish Date: 2009-10-19

Abstract

Abstract

The construction of available fusion models, such as that based on color space transform, on statistical, on multi-scale analysis is mostly based on the experience. These models have the problem that the parameters are subjectively chosen and can not be adjusted according to the follow applications of the fusion image. A image fusion framework of remote sensing is proposed based on data assimilation and particle swarm algorithm. Under this framework, weights of different attributes are determined according to their importance degree to the following process and object function is composed of the property weights, then the particle swarm algorithm is used to optimize the object function to get suitable image. A group experiments with the help of the entropy, mean gradient, standard variance, spatial frequency and structure similarity verify effectiveness of the framework.

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References(1)

References

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