We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network that trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality that ensures a very good performance also in the presence of a mismatch with respect to the training set and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and highquality CNN-based pansharpening of their own target images on general-purpose hardware.

Target-adaptive CNN-based pansharpening / Scarpa, Giuseppe; Vitale, Sergio; Cozzolino, Davide. - In: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. - ISSN 1558-0644. - 56:9(2018), pp. 5443-5457. [10.1109/TGRS.2018.2817393]

Target-adaptive CNN-based pansharpening

Giuseppe Scarpa;Davide Cozzolino
2018

Abstract

We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network that trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality that ensures a very good performance also in the presence of a mismatch with respect to the training set and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and highquality CNN-based pansharpening of their own target images on general-purpose hardware.
2018
Target-adaptive CNN-based pansharpening / Scarpa, Giuseppe; Vitale, Sergio; Cozzolino, Davide. - In: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. - ISSN 1558-0644. - 56:9(2018), pp. 5443-5457. [10.1109/TGRS.2018.2817393]
File in questo prodotto:
File Dimensione Formato  
08334206-2.pdf

solo utenti autorizzati

Descrizione: Articolo principale
Tipologia: Documento in Post-print
Licenza: Accesso privato/ristretto
Dimensione 4.86 MB
Formato Adobe PDF
4.86 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/711988
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 311
  • ???jsp.display-item.citation.isi??? 285
social impact