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Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques

Shivam Pande

TL;DR

This study addresses the challenges of high dimensionality and limited spatial resolution by employing deep learning techniques to efficiently process, extract features, and classify data in an integrated manner, and tackles the issue of limited training samples through self-supervised learning methods.

Abstract

Hyperspectral imaging provides precise classification for land use and cover due to its exceptional spectral resolution. However, the challenges of high dimensionality and limited spatial resolution hinder its effectiveness. This study addresses these challenges by employing deep learning techniques to efficiently process, extract features, and classify data in an integrated manner. To enhance spatial resolution, we integrate information from complementary modalities such as LiDAR and SAR data through multimodal learning. Moreover, adversarial learning and knowledge distillation are utilized to overcome issues stemming from domain disparities and missing modalities. We also tailor deep learning architectures to suit the unique characteristics of HSI data, utilizing 1D convolutional and recurrent neural networks to handle its continuous spectral dimension. Techniques like visual attention and feedback connections within the architecture bolster the robustness of feature extraction. Additionally, we tackle the issue of limited training samples through self-supervised learning methods, employing autoencoders for dimensionality reduction and exploring semi-supervised learning techniques that leverage unlabeled data. Our proposed approaches are evaluated across various HSI datasets, consistently outperforming existing state-of-the-art techniques.

Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques

TL;DR

This study addresses the challenges of high dimensionality and limited spatial resolution by employing deep learning techniques to efficiently process, extract features, and classify data in an integrated manner, and tackles the issue of limited training samples through self-supervised learning methods.

Abstract

Hyperspectral imaging provides precise classification for land use and cover due to its exceptional spectral resolution. However, the challenges of high dimensionality and limited spatial resolution hinder its effectiveness. This study addresses these challenges by employing deep learning techniques to efficiently process, extract features, and classify data in an integrated manner. To enhance spatial resolution, we integrate information from complementary modalities such as LiDAR and SAR data through multimodal learning. Moreover, adversarial learning and knowledge distillation are utilized to overcome issues stemming from domain disparities and missing modalities. We also tailor deep learning architectures to suit the unique characteristics of HSI data, utilizing 1D convolutional and recurrent neural networks to handle its continuous spectral dimension. Techniques like visual attention and feedback connections within the architecture bolster the robustness of feature extraction. Additionally, we tackle the issue of limited training samples through self-supervised learning methods, employing autoencoders for dimensionality reduction and exploring semi-supervised learning techniques that leverage unlabeled data. Our proposed approaches are evaluated across various HSI datasets, consistently outperforming existing state-of-the-art techniques.
Paper Structure (132 sections, 74 equations, 103 figures, 58 tables)

This paper contains 132 sections, 74 equations, 103 figures, 58 tables.

Table of Contents

  1. Introduction
  2. Literature review
  3. LULC classification with remotely sensed data
  4. Convolutional architectures
  5. Convolutional architectures with residual connections
  6. Attention mechanisms in CNNs
  7. Feedback connections in CNNs
  8. Effect of loss functions in classification
  9. Domain adaptation in remote sensing
  10. Multimodal data fusion in remote sensing
  11. Deep learning for remote sensing data fusion
  12. Learning with privileged information (LUPI)
  13. LUPI with modality distillation
  14. Self-supervised learning in remote sensing
  15. Generative tasks in SSL for remote sensing
  16. ...and 117 more sections

Figures (103)

  • Figure 1: Illustration of a Hyperspectral Cube to showcase the contiguous spectrum of the multiple channels yusuf2018survey.
  • Figure 2: Indian Pines hyperspectral dataset with (a) True colour composite (b) Groundtruth map
  • Figure 3: Indian Pines 2010 hyperspectral dataset with (a) True colour composite (b) Groundtruth map
  • Figure 4: Salinas Valley hyperspectral dataset with (a) True colour composite (b) Groundtruth map
  • Figure 5: Pavia University hyperspectral dataset with (a) True colour composite (b) Groundtruth map
  • ...and 98 more figures