PERCEPTUAL-BASED IMAGE FUSION FOR HYPERSPECTRAL DATA

Three hierarchical multiresolution image fusion techniques are impleme nted and tested using image data from the Airborne Visual/Infrared Ima ging Spectrometer (AVIRIS) hyperspectral sensor, The methods presented focus on combining multiple images from the AVIRIS sensor into a smal ler subset of images while maintaining the visual information necessar y for human analysis, Two of the techniques are published algorithms t hat were originally designed to combine images from multiple sensors, but are shown to work well on multiple images from the same sensor, Th e third method presented H as developed specifically to fuse hyperspec tral images for visual analysis, This new method uses the spatial freq uency response (contrast sensitivity) of the human visual system to de termine which features in the input images need to be preserved in the composite image(s) thus ensuring the composite image maintains the vi sually relevant features from each input image, The image fusion algor ithms are analyzed using test images with known image characteristics and image data from the AVIRIS hyperspectral sensor, After analyzing t he signal-to-noise ratios and visual aesthetics of the fused images, c ontrast sensitivity based fusion is shown to provide excellent fusion results and, in every case, outperformed the other two methods.