Hyperspectral image fusion using empirical mode decomposition

The technology of hyperspectral remote sensing, as an advanced technology in remote sensing, has been found wide application in many fields. However, the massive and high dimension data produce a challenge during its processing and analysis. Hyperspectral image fusion is rising as a new method which results from this background. The fused image would have enhanced information which is more understandable and decipherable for object recognition accurately. In this paper, we propose a novel method for image fusion and enhancement, using Empirical Mode Decomposition (EMD). EMD is a new data analysis method which expresses the tendency of signals at different scales by decomposing any complicated signal into a set of Intrinsic Mode Functions (IMFs). In this method, we decompose images from different hyperspectral band into their IMFs, and perform image fusion at the decomposition level. Based on an empirical understanding of the nature of the IMF, we devise adaptive weighting schemes which emphasize features from different band image, thereby increasing the information and visual content of the fused image.