Remote sensing image fusion based on Bayesian linear estimation

• 作 者: GE ZhiRong WANG Bin ZHANG LiMi
• 机 构: Department of Electronics Engineering,Key Laboratory of Wave Scattering and Remote Sensing Informati
• 出 处: 中国科学F辑（英文版）
• 发表日期: 2004年期
• 关键词: image fusion Bayesian linear estimation observation model Landsat ETM+
• 基金项目: 国家自然科学基金;国家重点基础研究发展计划(973计划);上海市自然科学基金;HangTian Support Techniques Foundation

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.