Hyperspectral pan-sharpening: a variational convex constrained formulation to impose parallel level lines, solved with ADMM
This work addresses hyperspectral image fusion for remote sensing applications, presenting an incremental improvement in method formulation and optimization.
The paper tackles hyperspectral pan-sharpening by fusing low-resolution hyperspectral and high-resolution panchromatic images using a variational convex constrained formulation that imposes parallel level lines, solved with ADMM, resulting in an efficient optimization scheme for this high-dimensional problem.
In this paper, we address the issue of hyperspectral pan-sharpening, which consists in fusing a (low spatial resolution) hyperspectral image HX and a (high spatial resolution) panchromatic image P to obtain a high spatial resolution hyperspectral image. The problem is addressed under a variational convex constrained formulation. The objective favors high resolution spectral bands with level lines parallel to those of the panchromatic image. This term is balanced with a total variation term as regularizer. Fit-to-P data and fit-to-HX data constraints are effectively considered as mathematical constraints, which depend on the statistics of the data noise measurements. The developed Alternating Direction Method of Multipliers (ADMM) optimization scheme enables us to solve this problem efficiently despite the non differentiabilities and the huge number of unknowns.