Hyperspectral pansharpening using convex optimization and collaborative total variation regularization

Paolo Addesso (1) , Mauro Dalla Mura (2) , Laurent Condat (3) , Rocco Restaino (1) , G Vivone (1) , D Picone (1) , Jocelyn Chanussot (2)

1 UNISA - Università degli Studi di Salerno = University of Salerno
2 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
3 GIPSA-AGPIG - GIPSA - Architecture, Géométrie, Perception, Images, Gestes

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.

Mots clés: Data fusion; Hyperspectral pansharpening; Convex optimization; Total variation; Deconvolution

Domaines: Traitement du signal et de l'image [eess.SP]

Paolo Addesso, Mauro Dalla Mura, Laurent Condat, Rocco Restaino, G Vivone, et al.. Hyperspectral pansharpening using convex optimization and collaborative total variation regularization. WHISPERS 2016 - 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Aug 2016, Los Angeles, United States. ⟨hal-01399399⟩