Hyperspectral-Multispectral Image Fusion via Tensor Ring and Subspace Decompositions

Xu, Honghui; Qin, Mengjie; Chen, Shengyong; Zheng, Yinqiang; Zheng, Jian wei

doi:10.1109/jstars.2021.3108233

Cited by 32 publications

(10 citation statements)

References 50 publications

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“…[25] probes into the intrinsic relationship between HRHS and LRMS images. The approach in [26] develops a novel low-rank tensor ring decomposition for HSI super-resolution. By exploiting the idea from the field of tensor completion, Ref.…”

Section: Tensor Decomposition Based Methodsmentioning

confidence: 99%

Hyperspectral Image Superresolution via Subspace-Based Deep Prior Regularization

Zheng

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral imaging is able to provide a finer delivery of various material properties than conventional imaging systems. Yet in reality, an optical system can only generate data with high spatial resolution but low spectral one, or vice versa, at video rates. As a result, an issue that fuses lowresolution hyperspectral (LRHS) and high-resolution multispectral (HRMS) images has gained great attention. However, most fusion approaches depend purely on hand-crafted regularizers or data-driven priors, leading to the issues of tricky parameter selection or poor interpretability. In this work, a subspace-based deep prior regularization (SDPR) is proposed to tackle these problems, which takes both hand-crafted regularizer and datadriven prior into account. Specifically, we leverage the spectral correlation of the images and transfer them from the original space to the subspace domain, within which a modified U-netbased deep prior learning network (SDPL-net) is designed for the fusion issue. Moreover, instead of taking the output of SDPL-net directly as the result, we further feed the output back to the model-based optimization. Under such prior regularization, the recovered high-resolution hyperspectral (HRHS) image holds a high consistency to its inherent structure and hence tends to present enhanced reliability and accuracy. Experimental results on simulated and real data reveal that the proposed method excels other state-of-the-art (SOTA) methods in both quantitative and qualitative metrics.

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Section: Tensor Decomposition Based Methodsmentioning

confidence: 99%

Hyperspectral Image Superresolution via Subspace-Based Deep Prior Regularization

Zheng

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Chen et al [99] presented a factor-smonthed TR decomposition (FSTRD) to capture the spatial-spectral continuity of HR-HS images. Based on the basic CTRF model, Xu et al [100] advocated LR TR decomposition based on TNN (LRTRTNN), which exploited the LR properties of non-local similar patches and their TR factors.…”

Section: ) Tr Decomposition Modelmentioning

confidence: 99%

Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review

Wang¹,

Hong²,

Han³

et al. 2022

Preprint

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Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing (RS) imaging has provided a significant amount of spatial and spectral information for the observation and analysis of the Earth's surface at a distance of data acquisition devices, such as aircraft, spacecraft, and satellite. The recent advancement and even revolution of the HS RS technique offer opportunities to realize the full potential of various applications, while confronting new challenges for efficiently processing and analyzing the enormous HS acquisition data. Due to the maintenance of the 3-D HS inherent structure, tensor decomposition has aroused widespread concern and research in HS data processing tasks over the past decades. In this article, we aim at presenting a comprehensive overview of tensor decomposition, specifically contextualizing the five broad topics in HS data processing, and they are HS restoration, compressed sensing, anomaly detection, superresolution, and spectral unmixing. For each topic, we elaborate on the remarkable achievements of tensor decomposition models for HS RS with a pivotal description of the existing methodologies and a representative exhibition on the experimental results. As a result, the remaining challenges of the follow-up research directions are outlined and discussed from the perspective of the real HS RS practices and tensor decomposition merged with advanced priors and even with deep neural networks. This article summarizes different tensor decomposition-based HS data processing methods and categorizes them into different classes from simple adoptions to complex combinations with other Manuscript received XX, 2022; revised XX, 2022.

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“…Then, Dian et al [42] proposed a method based on low-rank subspace to estimate the spectral dictionary and coefficients. Later, Xu et al [43] proposed a non-convex rank constrained fusion model of tensor ring factors, which imposed tensor nuclear norm constraints on decomposition factors. Inspired by t-prodcut based tensor sparse representation [44], Xu et al [45] proposed a new HSI super-resolution framework.…”

Section: Fusion Based On Tensor Decompositionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution Method Based on Spectral Smoothing Prior and Tensor Tubal Row-Sparse Representation

2022

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Due to the limited hardware conditions, hyperspectral image (HSI) has a low spatial resolution, while multispectral image (MSI) can gain higher spatial resolution. Therefore, derived from the idea of fusion, we reconstructed HSI with high spatial resolution and spectral resolution from HSI and MSI and put forward an HSI Super-Resolution model based on Spectral Smoothing prior and Tensor tubal row-sparse representation, termed SSTSR. Foremost, nonlocal priors are applied to refine the super-resolution task into reconstructing each nonlocal clustering tensor. Then per nonlocal cluster tensor is decomposed into two sub tensors under the tensor t-prodcut framework, one sub-tensor is called tersor dictionary and the other is called tensor coefficient. Meanwhile, in the process of dictionary learning and sparse coding, spectral smoothing constraint is imposed on the tensor dictionary, and L1,1,2 norm based tubal row-sparse regularizer is enforced on the tensor coefficient to enhance the structured sparsity. With this model, the spatial similarity and spectral similarity of the nonlocal cluster tensor are fully utilized. Finally, the alternating direction method of multipliers (ADMM) was employed to optimize the solution of our method. Experiments on three simulated datasets and one real dataset show that our approach is superior to many advanced HSI super-resolution methods.

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Hyperspectral-Multispectral Image Fusion via Tensor Ring and Subspace Decompositions

Cited by 32 publications

References 50 publications

Hyperspectral Image Superresolution via Subspace-Based Deep Prior Regularization

Hyperspectral Image Superresolution via Subspace-Based Deep Prior Regularization

Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review

Hyperspectral Image Super-Resolution Method Based on Spectral Smoothing Prior and Tensor Tubal Row-Sparse Representation

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