PSCSC-Net: A Deep Coupled Convolutional Sparse Coding Network for Pansharpening

Given a low-resolution multispectral (MS) image and a high-resolution panchromatic image, the task of pansharpening is to generate a high-resolution MS image. Deep learning (DL)-based methods receive extensive attention recently. Different from the existing DL-based methods, this article proposes a...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing Jg. 60; S. 1 - 16
1. Verfasser: Yin, Haitao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Artificial neural networks
Coding
Convolutional sparse coding (CSC)
Deep learning
deep neural network
deep unfolding
High resolution
Image resolution
Iterative algorithms
Iterative methods
Machine learning
Neural networks
Optimization
Pansharpening
Resolution
Satellites
Signal resolution
Spatial resolution
Training
ISSN:0196-2892, 1558-0644
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Beschreibung
Zusammenfassung:Given a low-resolution multispectral (MS) image and a high-resolution panchromatic image, the task of pansharpening is to generate a high-resolution MS image. Deep learning (DL)-based methods receive extensive attention recently. Different from the existing DL-based methods, this article proposes a novel deep neural network for pansharpening inspired by the learned iterative soft thresholding algorithm. First, a coupled convolutional sparse coding-based pansharpening (PSCSC) model and related traditional optimization algorithm are proposed. Then, following the procedures of traditional algorithm for solving PSCSC, an interpretable end-to-end deep pansharpening network is developed using a deep unfolding strategy. The designed deep architecture can also be understood in the view of details injection (DI)-based scheme. This work offers a solution that integrates the DL-, DI-, and variational optimization-based schemes into a framework. The experimental results on the reduced- and full-scale datasets demonstrate that the proposed deep pansharpening network outperforms popular traditional methods and some current DL-based methods.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3088313