PROJECT TITLE :

A Multivariate Empirical Mode DecompositionBased Approach to Pansharpening

ABSTRACT:

We propose a completely unique class of schemes for the pansharpening of multispectral (MS) pictures employing a multivariate empirical mode decomposition (MEMD) algorithm. MEMD is an extension of the empirical mode decomposition (EMD) algorithm, which enables the decomposition of multivariate data into its intrinsic oscillatory scales. The ability of MEMD to process multichannel knowledge directly by performing data-driven, local, and multiscale analysis makes it a perfect match for pansharpening applications, a task for that customary univariate EMD is ill-equipped thanks to the nonuniqueness, mode-mixing, and mode-misalignment problems. We have a tendency to show that MEMD overcomes the constraints of standard EMD and yields improved spatial and spectral performance within the context of pansharpening of MS pictures. The potential of the proposed schemes is more demonstrated through comparative analysis against a range of standard pansharpening algorithms on both simulated Pleiades and real-world IKONOS knowledge sets.