A novel remote sensing image fusion method based on independent component analysis

Abstract

Many research papers have reported problems with existing fusion techniques. The most significant problem is that fusion images produce spectral distortion when they retain high spatial resolution. Independent component analysis (ICA) can eliminate low-order and also high-order redundancy for data. According to statistical theory, the most important information from data is always included in the statistical characteristic of high order. Therefore, independent components (ICs) resulting from the transform of the ICA provide information on the data that is otherwise hidden in the large data set, and describe the essential structure of the data. For a colour or false colour composite image, the ICs represent the main body, spectral and spatial detail information. In this study, a novel remote sensing image fusion method based on ICA is presented. By replacing the main body IC of a multispectral image with the vector of a panchromatic (PAN) image, the new ICs contain both spatial information of the PAN image and spectral characteristics of the multispectral image. Then an inverse ICA transformation (IIT) is performed to attain the fusion image. IKONOS and Enhanced Thematic Mapper Plus (ETM + ) images are used to evaluate the proposed method and others (hue–saturation–value (HSV), principal component analysis and wavelets). The fusion results are compared graphically, visually and statistically, and show that the proposed method can retain spatial and spectral information simultaneously, and has a better balance between them.