Highly optimized weighted-IHS pan-sharpening with edge-preserving denoising

The interpretation of satellite imagery benefits from merging the spatial structure of the high-resolution panchromatic image with the spectral information. Such "pan-sharpening" has been the topic of extensive research. One objective of our investigations is to process satellite images within seconds. In this work, we build upon the "Fast IHS" technique, using a weighted linear combination of the up-sampled multispectral bands to derive a composite image closer to what the panchromatic sensor had seen. The difference to the actual panchromatic image approximates the high-frequency detail signal and is added to the multispectral bands. However, fixed band weights (exemplified by the "Modified IHS" algorithm) cannot account for differing radiometry and atmospheric conditions. To further reduce color distortion, we compute the optimal band weights for a given data set in the sense of minimizing the mean-square difference between the composite and panchromatic images. Since the noise in the panchromatic image (sometimes non-linear) impacts a subsequent graphbased segmentation algorithm, an additional denoising step is applied before fusion. We use an improved approximation of the Bilateral Filter, which preserves edges and requires only one fast iteration. The quality of the fused image is evaluated in a comparative study of pan-sharpening algorithms available in ERDAS IMAGINE 9.3. Objective metrics such as Q4 show an improvement in terms of color fidelity. The image segmentation results also demonstrate the applicability of this method towards automated image analysis