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 spatial 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.
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Supported in part by the 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), and Shanghai NSF (Grant No. 04ZR14018)
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Ge, Z., Wang, B. & Zhang, L. Remote sensing image fusion based on Bayesian linear estimation. SCI CHINA SER F 50, 227–240 (2007). https://doi.org/10.1007/s11432-007-0008-7
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DOI: https://doi.org/10.1007/s11432-007-0008-7