Perceptual Based Image Fusion with Applications to Hyperspectral Image Data

摘要

Development of new imaging sensors has created a need for image processingtechniques that can fuse images from different sensors or multiple images produced by the same sensor. The methods presented here focus on combining image data from the Airborne Visual and Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor into a single or smaller subset of images while maintaining the visual information necessary for human analysis. Three hierarchical multi-resolution image fusion techniques are implemented and tested using the AVIRIS image data and test images that contain various levels of correlated or uncorrelated noise. Two of the algorithms are published fusion methods that combine images from multiple sensors. The third method was developed to fuse any co-registered image data. This new method uses the spatial frequency response (contrast sensitivity) of the human visual system to determine which parts of the input images contain the salient features that need to be preserved in the composite image(s). After analyzing the signal-to-noise ratios and visual aesthetics of the fused images, contrast sensitivity based fusion is shown to provide excellent fusion results and, in every case, clearly outperformed the other two methods. Finally, as an illustrative example of how the fusion techniques are independent of the hyperspectral application, they are applied to fusing multiple polarimetric images from a Synthetic Aperture Radar to enhance automated targeting techniques.