Awesome Hyperspectral Deep Learning

A curated list of papers and ressources linked to Deep Learning analysis of Hyperspectral Images

Contents

• Remote Sensing
  • Supervised
  • Unsupervised
• Medical
• Multi-Spectral Surgical Imaging
• Misc
• Bibliography Tool
• General
  • Data Augmentation
  • Loss Functions
  • Normalization
  • Anomaly Detection
  • HSI distance metrics

Remote Sensing

Supervised

• Deep learning classifiers for hyperspectral imaging: A review (2020), Paoletti et al. [html]
• Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field (2019), Liang et al. [pdf]
• Deep Learning for Hyperspectral Image Classification on Embedded Platforms (2018), Balakrishnan et al. [html]
• Hyperspectral Image Classification With Convolutional Neural Network and Active Learning (2020), Cao et al. [html]
• Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review (2019), Signoroni et al. [pdf]
• Learning Hyperspectral Feature Extraction and Classification with ResNeXtNetwork (2020), Nyasaka et al. [pdf]
• HybridSN: Exploring 3D-2D CNN FeatureHierarchy for Hyperspectral Image Classification (2019), Roy et al. [pdf]
• Spectral–spatial residual network for hyperspectral image classification: A 3-D deep learning framework (2017), Zhong et al. [pdf]
• Deep Convolutional Capsule Network for Hyperspectral Image Spectral and Spectral-Spatial Classification (2019), Zhu et al. [pdf]
• Learning Deep Hierarchical Spatial–Spectral Features for Hyperspectral Image Classification Based on Residual 3D-2D CNN (2019), Feng et al. [html]
• Deep Convolutional Neural Networks for Hyperspectral Image Classification (2015), Hu et al. [pdf]
• DEEP LEARNING APPROACH FOR REMOTE SENSING IMAGE ANALYSIS (2016), Ben Hamida et al. [html]
• Deep Learning for Classification of Hyperspectral Data: A Comparative Review (2019), Audebert et al. [pdf]
• Deep Recurrent Neural Networks for Hyperspectral Image Classification (2017), Mou et al. [pdf]
• Deep supervised learning for hyperspectral data classification through convolutional neural networks (2015), Makantasis et al. [pdf]
• Going Deeper with Contextual CNN for Hyperspectral Image Classification (2017), Lee et Kwon [pdf]
• New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning (2017), Ghamisi et al. [pdf]
• Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network (2017), Li et al. [pdf]

Unsupervised

• Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging (2016), Zabalza et al. [html]
• Semi-supervised classification of hyperspectral imagery based on stacked autoencoders (2016), Fu et al. [html]
• Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network (2015), Chen et al. [pdf]
• Unsupervised Spectral–Spatial Feature Learning via Deep Residual Conv–Deconv Network for Hyperspectral Image Classification (2018), Mou et al. [pdf]

Medical

• Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations (2018), Fabelo et al. [html]
• In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer (2019), Halicek et al. [html]
• Hyperspectral image enhancement and mixture deep-learning classification of corneal epithelium injuries (2017), Noor et al. [pdf]
• Tumor semantic segmentation in hyperspectral images using deep learning (2019), Trajanovski et al. [[pdf]](Tumor semantic segmentation in hyperspectral images using deep learning)
• Medical hyperspectral imaging: a review (2014), Lu et Fei [pdf]
• A Dual Stream Network for Tumor Detection in Hyperspectral Images (2019), Weijtmans et al. [pdf]
• Cell classification using convolutional neural networks in medical hyperspectral imagery (2017), Xiang Li et al. [html]
• Convolutional neural network for medical hyperspectral image classification with kernel fusion (2018), Huang et al. [html]
• Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging (2017), Halicek et al. [pdf]
• Medical Hyperspectral Image Classification Based on End-to-End Fusion Deep Neural Network (2017), Wei et al. [html]
• Hyperspectral Tissue Image Segmentation usingSemi-Supervised NMF and Hierarchical Clustering (2018), Kumar et al. [html]

Multi-Spectral Surgical Imaging

• Surgical spectral imaging (2020), Clancy et al. [html]

Misc

• HYPerspectral Enhanced Reality (HYPER): a physiology-based surgical guidance tool (2019), Barberio et al. [html]
• Hyperspectral enhanced reality (HYPER) for anatomical liver resection (2020), Urade et al. [html]
• Indocyanine-green-loaded microballoons for biliary imaging in cholecystectomy (2012), Mitra et al. [html]

Bibliography tool

• Announcing Connected Papers — a visual tool for researchers to find and explore academic papers [html]
• Connected Papers [html]

General

Data Augmentation

• Hyperspectral Data Augmentation (2019), Nalepa et al. [pdf]
• On data augmentation for segmenting hyperspectral images (2019), Nalepa et al. [pdf]

Loss functions

• Loss Functions for Medical Image Segmentation: A Taxonomy[html]

Normalization

• Does normalization methods play a role for hyperspectral image classification? (2017), Cao et al. [pdf]
• Advanced Preprocessing: Sample Normalization [html]

Anomaly detection

• Discriminative Reconstruction Constrained Generative Adversarial Network for Hyperspectral Anomaly Detection (2020), Jiang et al. [html]

HSI distance metrics

• A Comprehensive Evaluation of Spectral Distance Functions and Metrics for Hyperspectral Image Processing (2015), Deborah et al. [pdf]

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License

To the extent possible under law, Gaël Mukunde has waived all copyright and related or neighboring rights to this work.