Spectral-Fidelity Convolutional Neural Networks for Hyperspectral Pansharpening

Hyperspectral (HS) pansharpening aims at fusing a low-resolution HS (LRHS) image with a panchromatic image to obtain a full-resolution HS image. Most of the existing HS pansharpening approaches are usually based on traditional multispectral pansharpening techniques, which are not especially tailored...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2020, Vol.13, p.5898-5914
Main Authors: He, Lin, Zhu, Jiawei, Li, Jun, Meng, Deyu, Chanussot, Jocelyn, Plaza, Antonio
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Convolutional neural networks (CNNs)
Engineering Sciences
Feature extraction
Fidelity
hierarchical detail reconstruction
hyperspectral image
Hyperspectral imaging
Image reconstruction
Image resolution
Kernel
Neural networks
pansharpening
Resolution
Signal and Image processing
Spatial resolution
Spectra
Spectral bands
spectral-fidelity loss
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Summary:Hyperspectral (HS) pansharpening aims at fusing a low-resolution HS (LRHS) image with a panchromatic image to obtain a full-resolution HS image. Most of the existing HS pansharpening approaches are usually based on traditional multispectral pansharpening techniques, which are not especially tailored for two inherent challenges of the HS pansharpening, i.e., much wider spectral range gap between the two kinds of images and having to recover details in many continuous spectral bands simultaneously. In this article, we develop new spectral-fidelity convolutional neural networks (called HSpeNets) for HS pansharpening to keep the fidelity of a pansharpened image to its true spectra as much as possible. Our methods particularly focus on the decomposability of HS details, accordingly synthesizing these details progressively, and meanwhile introduce a spectral-fidelity loss. We give theoretical justifications and provide detailed experimental results, showing the superiorities of the proposed HSpeNets with regard to other state-of-the-art pansharpening approaches.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2020.3025040