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Hyperspectral and Multispectral Image Fusion using Cluster-based Multi-branch BP Neural Networks

1
Institute for Artificial Intelligence, Beijing National Research Center for Information Science and Technology, State Key Laboratory of Intelligent Technology and Systems, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
2
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
3
School of Opte-electronics, Beijing Institute of Technology, Beijing100081, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(10), 1173; https://doi.org/10.3390/rs11101173
Received: 22 April 2019 / Revised: 11 May 2019 / Accepted: 13 May 2019 / Published: 16 May 2019
(This article belongs to the Special Issue Quality Improvement of Remote Sensing Images)
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Abstract

Fusion of the high-spatial-resolution hyperspectral (HHS) image using low-spatial- resolution hyperspectral (LHS) and high-spatial-resolution multispectral (HMS) image is usually formulated as a spatial super-resolution problem of LHS image with the help of an HMS image, and that may result in the loss of detailed structural information. Facing the above problem, the fusion of HMS with LHS image is formulated as a nonlinear spectral mapping from an HMS to HHS image with the help of an LHS image, and a novel cluster-based fusion method using multi-branch BP neural networks (named CF-BPNNs) is proposed, to ensure a more reasonable spectral mapping for each cluster. In the training stage, considering the intrinsic characteristics that the spectra are more similar within each cluster than that between clusters and so do the corresponding spectral mapping, an unsupervised clustering is used to divide the spectra of the down-sampled HMS image (marked as LMS) into several clusters according to spectral correlation. Then, the spectrum-pairs from the clustered LMS image and the corresponding LHS image are used to train multi-branch BP neural networks (BPNNs), to establish the nonlinear spectral mapping for each cluster. In the fusion stage, a supervised clustering is used to group the spectra of HMS image into the clusters determined during the training stage, and the final HHS image is reconstructed from the clustered HMS image using the trained multi-branch BPNNs accordingly. Comparison results with the related state-of-the-art methods demonstrate that our proposed method achieves a better fusion quality both in spatial and spectral domains.
Keywords: image fusion; spectral mapping; multi-branch BP neural networks; cluster-based learning image fusion; spectral mapping; multi-branch BP neural networks; cluster-based learning
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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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Han, X.; Yu, J.; Luo, J.; Sun, W. Hyperspectral and Multispectral Image Fusion using Cluster-based Multi-branch BP Neural Networks. Remote Sens. 2019, 11, 1173.

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