mlsanityData to 7 Oct 2026

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

arXiv 2205.06407 ↗First posted 13 May 2022cs.CV cs.LG eess.IVcited by 9

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Abstract (arXiv)

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, super-resolution, 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 priors for the algorithm beginners. We also expect this survey can provide new investigations and development trends for the experienced researchers who understand tensor decomposition and HS RS to some extent.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 35 of its 255 references that are in the map. First posted on arXiv 13 May 2022.

Builds on

35 of 255 references.

References · 35

  1. 2016Tensor Decomposition for Signal Processing and Machine Learningcited by 223
  2. 2021SpectralFormer: Rethinking Hyperspectral Image Classification with Transformerscited by 168
  3. 2020More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classificationcited by 126
  4. 2016Tensor Ring Decompositioncited by 125
  5. 2020Graph Convolutional Networks for Hyperspectral Image Classificationcited by 113
  6. 2022Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Reviewcited by 73
  7. 2018An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixingcited by 65
  8. 2017Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decompositioncited by 61
  9. 2014Novel methods for multilinear data completion and de-noising based on tensor-SVDcited by 56
  10. 2015Hyperspectral pansharpening: a reviewcited by 55
  11. 2014A convex formulation for hyperspectral image superresolution via subspace-based regularizationcited by 48
  12. 2018Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoisingcited by 39
23 more
  1. 2017Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimizationcited by 35
  2. 2017Weighted Low-rank Tensor Recovery for Hyperspectral Image Restorationcited by 30
  3. 2021FCCDN: Feature Constraint Network for VHR Image Change Detectioncited by 25
  4. 2021Image Restoration for Remote Sensing: Overview and Toolboxcited by 25
  5. 2018Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approachcited by 24
  6. 2014Hyperspectral and Multispectral Image Fusion based on a Sparse Representationcited by 23
  7. 2018Tensor N-tubal rank and its convex relaxation for low-rank tensor recoverycited by 19
  8. 2021Self-Supervised Nonlinear Transform-Based Tensor Nuclear Norm for Multi-Dimensional Image Recoverycited by 16
  9. 2015Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurementscited by 14
  10. 2020Hyperspectral Super-Resolution via Coupled Tensor Ring Factorizationcited by 12
  11. 2019Algorithms and Comparisons of Non-negative Matrix Factorization with Volume Regularization for Hyperspectral Unmixingcited by 10
  12. 2019Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classificationcited by 10
  13. 2020Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modelingcited by 10
  14. 2018Hyperspectral Super-Resolution with Coupled Tucker Approximation: Recoverability and SVD-based algorithmscited by 9
  15. 2021Using Low-rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixingcited by 6
  16. 2018Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variabilitycited by 5
  17. 2016Robust Fusion of Multi-Band Images with Different Spatial and Spectral Resolutions for Change Detectioncited by 4
  18. 2018Non-local tensor completion for multitemporal remotely sensed images inpaintingcited by 4
  19. 2018A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixingcited by 4
  20. 2015Robust Linear Spectral Unmixing using Anomaly Detectioncited by 3
  21. 2019Hyperspectral Super-resolution: A Coupled Nonnegative Block-term Tensor Decomposition Approachcited by 2
  22. 2014Bayesian nonlinear hyperspectral unmixing with spatial residual component analysiscited by 1
  23. 2019A Dual Symmetric Gauss-Seidel Alternating Direction Method of Multipliers for Hyperspectral Sparse Unmixingcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 2; 2026: 2.

9 papers.

  1. 2023Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Reviewcited by 41
  2. 2023Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Packagecited by 12
  3. 2024Unsupervised Hyperspectral and Multispectral Image Blind Fusion Based on Deep Tucker Decomposition Network with Spatial-Spectral Manifold Learningcited by 3
  4. 2024Irregular Tensor Low-Rank Representation for Hyperspectral Image Representationcited by 3
  5. 2025STAR-Net: An Interpretable Model-Aided Network for Remote Sensing Image Denoisingcited by 2
  6. 2024Generalized Least Squares Kernelized Tensor Factorizationcited by 1
  7. 2025Compact Multi-level-prior Tensor Representation for Hyperspectral Image Super-resolutioncited by 0
  8. 2026Structured Adaptive Tensor Prediction for Streaming Datacited by 0
  9. 2026Convolution-Free Holistic Multivariance Decomposition Layer for Efficient Hyperspectral Image Classification Tensor Networkscited by 0