A New Developed GIHS-BT-SFIM Fusion Method Based On Edge and Class Data

The objective of image fusion (or sometimes pan sharpening) is to produce a single image containing the best aspects of the source images. Some desirable aspects are high spatial resolution and high spectral resolution. With the development of space borne imaging sensors, a unified image fusion approach suitable for all employed imaging sources becomes necessary. Among various image fusion methods, intensity-hue-saturation (IHS) and Brovey Transforms (BT) can quickly merge huge amounts of imagery. However they often face color distortion problems with fused images. The SFIM fusion is one of the most frequently employed approaches in practice to control the tradeoff between the spatial and spectral information. In addition it preserves more spectral information but suffer more spatial information loss. Its effectiveness is heavily depends on the filter design. In this work, two modifications were tested to improve the spectral quality of the images and also investigating class-based fusion results. First, a Generalized Intensity-Hue-Saturation (GIHS), Brovey Transform (BT) and smoothing-filter based intensity modulation (SFIM) approach was implemented. This kind of algorithm has shown computational advantages among other fusion methods like wavelet, and can be extended to different number of bands as in literature discussed. The used IHS-BT-SFIM algorithm incorporates IHS, IHS-BT, BT, BT-SFIM and SFIM methods by two adjustable parameters. Second, a method was proposed to plus edge information in previous GIHS_BT_SFIM and edge enhancement by panchromatic image. Adding panchromatic data to images had no much improvement. Third, an edge adaptive GIHS_BT_SFIM was proposed to enforce fidelity away from the edges. Using MS image off edges has shown spectral improvement in some fusion methods. Fourth, a class based fusion was tested, which tests different coefficients for each method due to its class. The best parameters for vegetated areas was k1 = 0.6, k2 = 0.8; and for urban region it was k1 = 0.4, k2 = 0.4. Results might be useful for future studies on fusion methods and their generalization