Superresolution reconstruction of hyperspectral remote sensing imagery using constrained optimization of POCS

An extended superresolution observation model is proposed for POCS superresolution of hyperspectral images. Multiple constraint criteria based on a priori knowledge were incorporated: data consistence, amplitude constraint, Total Variation edge smoothing constraint, outlier rejection, and PCA based denoising. The constraint criteria are applied using POCS superresolution reconstruction. The method was tested with both simulation and multi-viewing hyperspectral CHRIS images. Preliminary results of the constraint based superresolution shows potential for angular hyperspectral images.