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Awesome Hyperspectral Deep LearningAwesome

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

Contents

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]

Contribute

Contributions welcome! Read the contribution guidelines first.

License

CC0

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