Multispectral and panchromatic image fusion based on improved bilateral filter

Abstract

Image fusion is of great importance to various remote sensing applications because many Earth observation satellites provide both high-resolution panchromatic (Pan) and low-resolution multispectral (MS) images. A number of fusion methods have been proposed, such as intensity-hue-saturation fusion and wavelet transform fusion methods. However, further studies are still necessary to improve the fusion performance for new types of remotely sensed images, such as IKONOS or QuickBird images. We propose an improved bilateral total variation filter method for fusing such MS and Pan images based on regularization. First, the constraints on the MS and Pan images are imposed based on the observation model. Then, the improved bilateral filter is used as an a priori model to constrain the high-resolution MS images. Finally, the steepest descent optimization algorithm is used to obtain the estimated MS images. Fusion simulations on spatially degraded IKONOS and QuickBird images, whose original MS images are available for reference, respectively, show that the proposed approach has better spatial quality while keeping the spectral information of the MS images.