Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 13, Issue -, Pages 5898-5914Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2020.3025040
Keywords
Feature extraction; Spatial resolution; Image reconstruction; Hyperspectral imaging; Kernel; Convolutional neural networks (CNNs); hierarchical detail reconstruction; hyperspectral image; pansharpening; spectral-fidelity loss
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Funding
- National Natural Science Foundation of China [62071184, 61771496, 61571195]
- Guangdong Provincial Natural Science Foundation [2016A030313254, 2016A030313516, 2017A030313382]
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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.
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