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Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing

arXiv 2103.01449 ↗First posted 2 Mar 2021cs.CV cs.AI eess.IVcited by 0

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Abstract (arXiv)

Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to process and analyze these hyperspectral (HS) products mainly by means of seasoned experts. However, with the ever-growing volume of data, the bulk of costs in manpower and material resources poses new challenges on reducing the burden of manual labor and improving efficiency. For this reason, it is, therefore, urgent to develop more intelligent and automatic approaches for various HS RS applications. Machine learning (ML) tools with convex optimization have successfully undertaken the tasks of numerous artificial intelligence (AI)-related applications. However, their ability in handling complex practical problems remains limited, particularly for HS data, due to the effects of various spectral variabilities in the process of HS imaging and the complexity and redundancy of higher dimensional HS signals. Compared to the convex models, non-convex modeling, which is capable of characterizing more complex real scenes and providing the model interpretability technically and theoretically, has been proven to be a feasible solution to reduce the gap between challenging HS vision tasks and currently advanced intelligent data processing models.

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References · 33

  1. 2017Non-convex Optimization for Machine Learningcited by 132
  2. 2020More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classificationcited by 126
  3. 2020Graph Convolutional Networks for Hyperspectral Image Classificationcited by 113
  4. 2014Deep Unfolding: Model-Based Inspiration of Novel Deep Architecturescited by 76
  5. 2018An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixingcited by 65
  6. 2017Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decompositioncited by 61
  7. 2015Hyperspectral pansharpening: a reviewcited by 55
  8. 2018Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Networkcited by 51
  9. 2014A convex formulation for hyperspectral image superresolution via subspace-based regularizationcited by 48
  10. 2020Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restorationcited by 43
  11. 2017Unrolled Optimization with Deep Priorscited by 40
  12. 2020Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)cited by 36
21 more
  1. 2019Cascaded Recurrent Neural Networks for Hyperspectral Image Classificationcited by 31
  2. 2020Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolutioncited by 29
  3. 2015Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equationcited by 25
  4. 2018Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approachcited by 24
  5. 2014Hyperspectral and Multispectral Image Fusion based on a Sparse Representationcited by 23
  6. 2020Guided Deep Decoder: Unsupervised Image Pair Fusioncited by 20
  7. 2019ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Featurescited by 18
  8. 2015Hyperspectral unmixing with spectral variability using a perturbed linear mixing modelcited by 17
  9. 2019Learnable Manifold Alignment (LeMA) : A Semi-supervised Cross-modality Learning Framework for Land Cover and Land Use Classificationcited by 17
  10. 2018CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondencescited by 15
  11. 2020Coupled Convolutional Neural Network with Adaptive Response Function Learning for Unsupervised Hyperspectral Super-Resolutioncited by 15
  12. 2020Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reductioncited by 14
  13. 2015Kernel Manifold Alignmentcited by 13
  14. 2015Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatchescited by 13
  15. 2019Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detectioncited by 13
  16. 2019Learning Shared Cross-modality Representation Using Multispectral-LiDAR and Hyperspectral Datacited by 12
  17. 2020Hyperspectral Super-Resolution via Coupled Tensor Ring Factorizationcited by 12
  18. 2019MIMA: MAPPER-Induced Manifold Alignment for Semi-Supervised Fusion of Optical Image and Polarimetric SAR Datacited by 7
  19. 2018Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variabilitycited by 5
  20. 2019Hybrid Inexact BCD for Coupled Structured Matrix Factorization in Hyperspectral Super-Resolutioncited by 2
  21. 2018Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classificationcited by 1

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