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Remote sensing image fusion based on Bayesian linear estimation

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【Author】 GE ZhiRong1, WANG Bin1,2? & ZHANG LiMing1 1 Department of Electronics Engineering, Fudan University, Shanghai 200433, China; 2 Key Laboratory of Wave Scattering and Remote Sensing Information (Ministry of Education), Fudan University, Shanghai 200433, China

【摘要】 A new remote sensing image fusion method based on statistical parameter estimation is proposed in this paper. More specially, Bayesian linear estimation (BLE) is applied to observation models between remote sensing images with different spa- tial and spectral resolutions. The proposed method only estimates the mean vector and covariance matrix of the high-resolution multispectral (MS) images, instead of assuming the joint distribution between the panchromatic (PAN) image and low-resolution multispectral image. Furthermore, the proposed method can enhance the spatial resolution of several principal components of MS images, while the traditional Principal Component Analysis (PCA) method is limited to enhance only the first principal component. Experimental results with real MS images and PAN image of Landsat ETM+ demonstrate that the proposed method performs better than traditional methods based on statistical parameter estimation, PCA-based method and wavelet-based method.

【Abstract】 A new remote sensing image fusion method based on statistical parameter estimation is proposed in this paper. More specially, Bayesian linear estimation (BLE) is applied to observation models between remote sensing images with different spa- tial and spectral resolutions. The proposed method only estimates the mean vector and covariance matrix of the high-resolution multispectral (MS) images, instead of assuming the joint distribution between the panchromatic (PAN) image and low-resolution multispectral image. Furthermore, the proposed method can enhance the spatial resolution of several principal components of MS images, while the traditional Principal Component Analysis (PCA) method is limited to enhance only the first principal component. Experimental results with real MS images and PAN image of Landsat ETM+ demonstrate that the proposed method performs better than traditional methods based on statistical parameter estimation, PCA-based method and wavelet-based method.

【基金】 National Natural Science Foundation of China (Grant Nos. 60672116 and 30370392);the Major State Basic Research Development Program of China (Grant No. 2001CB309400); HangTian Support Techniques Foundation (Grant No. 2004-1.3-03);Shanghai NSF (Grant No. 04ZR14018)
  • 【文献出处】 Science in China(Series F:Information Sciences) ,中国科学(F辑:信息科学)(英文版) , 编辑部邮箱 ,2007年02期
  • 【分类号】TP391.41;TP202.4
  • 【被引频次】14
  • 【下载频次】119
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