Collaborative total variation for hyperspectral pansharpening

Variational methods are widely used in image processing for problems ranging from denoising to data fusion. In this paper we focus on a recent regularization method, called Collaborative Total Variation, applied to the hyperspectral pansharpening, which deals with the fusion of low resolution hyperspectral and high resolution panchromatic images. The effectiveness of this novel approach is evaluated for different Collaborative Norms and the assessment is performed on the Pavia University dataset.

1. Introduction

HyperSpectral (HS) data usually consists of hundreds of spectral bands that aid the sampling of material spectral signatures of the landscape acquired by the particular sensor. These images are of great interest for several applications, such as precision agriculture or mineral mapping [1]. Unfortunately, the high spectral resolution of these instruments is usually paid by a coarse spatial resolution. On other hand, panchromatic (PAN) cameras have an elevate spatial resolution (e.g. the WorldView-3/−4 panchromatic sensor has a spatial resolution finer than 50 cm) at the price of a low spectral resolution (gray-scale images are provided).