Hyperspectral pansharpening is a challenging research area and several methods have been recently developed to fuse low resolution hyperspectral and high resolution panchromatic images. In this paper we focus on a recent regularization method, called Collaborative Total Variation, exploiting a convex optimization algorithm. We evaluate the effectiveness of this novel approach in comparison to existing methods, and assess the performances on two datasets: a synthetic scene mimicking the characteristics of the Hyperion and ALI sensors and the Pavia University dataset.

Hyperspectral pansharpening using convex optimization and collaborative total variation regularization

Addesso, P.;Dalla Mura, M.;Restaino, R.;Vivone, G.;Picone, D.;
2016-01-01

Abstract

Hyperspectral pansharpening is a challenging research area and several methods have been recently developed to fuse low resolution hyperspectral and high resolution panchromatic images. In this paper we focus on a recent regularization method, called Collaborative Total Variation, exploiting a convex optimization algorithm. We evaluate the effectiveness of this novel approach in comparison to existing methods, and assess the performances on two datasets: a synthetic scene mimicking the characteristics of the Hyperion and ALI sensors and the Pavia University dataset.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4707376
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