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Remote Sens. 2016, 8(7), 594; doi:10.3390/rs8070594

Pansharpening by Convolutional Neural Networks

Università di Napoli Federico II, Via Claudio 21, Napoli 80125, Italy
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Academic Editors: Lizhe Wang, Guoqing Zhou and Prasad S. Thenkabail
Received: 20 May 2016 / Revised: 4 July 2016 / Accepted: 8 July 2016 / Published: 14 July 2016
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Abstract

A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices typical of remote sensing. Experiments on three representative datasets show the proposed method to provide very promising results, largely competitive with the current state of the art in terms of both full-reference and no-reference metrics, and also at a visual inspection. View Full-Text
Keywords: multiresolution; segmentation; enhancement; super-resolution; machine learning; convolutional neural networks multiresolution; segmentation; enhancement; super-resolution; machine learning; convolutional neural networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Masi, G.; Cozzolino, D.; Verdoliva, L.; Scarpa, G. Pansharpening by Convolutional Neural Networks. Remote Sens. 2016, 8, 594.

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