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Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review

Alberto Signoroni, Mattia Savardi, Annalisa Baronio, Sergio Benini
2019 Journal of Imaging  
Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain.  ...  On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary  ...  (a) Autoencoders; (b) Deep belief networks. Network architecture of a Stacked Autoencoder Appendix A.5. Deep Belief Networks  ... 
doi:10.3390/jimaging5050052 pmid:34460490 fatcat:ledlmt42bfdtdhe7tvj2dl2rwm

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, Feng Xu, Friedrich Fraundorfer
2017 IEEE Geoscience and Remote Sensing Magazine  
In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously  ...  In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all?  ...  Siamese networks have also been recently applied [147] to detect changes between matched ground panoramas and aerial images.  ... 
doi:10.1109/mgrs.2017.2762307 fatcat:ec7b32lpdnhvzbdz2uoayw6anq

Front Matter: Volume 10615

Hui Yu, Junyu Dong
2018 Ninth International Conference on Graphic and Image Processing (ICGIP 2017)  
The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon.  ...  Please use the following format to cite material from these proceedings: Publication of record for individual papers is online in the SPIE Digital Library.  ...  pairwise co-regularization [10615-235] 10615 2Z Deep multi-scale convolutional neural network for hyperspectral image classification [10615-239] 10615 30 Different approaches for the texture classification  ... 
doi:10.1117/12.2316542 fatcat:tdaw76jq6nehpnttiga2lcuhna

Artificial Intellgence – Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021 [article]

Karl-Herbert Schäfer
2021 arXiv   pre-print
The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe  ...  , Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture  ...  Iraki for helpful comments and discussion.  ... 
arXiv:2112.05657v1 fatcat:wdjgymicyrfybg5zth2dc2i3ni

Deep component analysis: algorithms and applications

George Trigeorgis, Stefanos Zafeiriou, Bjoern Schuller, Engineering And Physical Sciences Research Council, Google (Firm)
2018
Component Analysis (CA) methods have been crucial contributors for the large success of machine learning over the past decades.  ...  That is to incorporate the power of neural networks with the statistical intuition and the specially crafted ideas of component analysis methods.  ...  each network f i for 0 < i < m can be tied (see Siamese networks [110] ).  ... 
doi:10.25560/58861 fatcat:jmnarhrwfvhpzio6t7sd2a3gda

Hubness in the protein sequence universe

Roman Vinzenz Feldbauer
2020 unpublished
In addition, deep networks are used to learn protein sequence vector representations, and investigated for orthologous group inference.  ...  While there is little evidence of improvements caused by hubness reductions, deep learning enables fast and accurate protein orthologous group inference.  ...  ACKNOWLEDGMENT We thank Jan Schlüter for fruitful discussions, and Thomas Rattei for computational resources. Acknowledgements We thank Silvan David Peter for testing the software.  ... 
doi:10.25365/thesis.64427 fatcat:wbai3saw7nbwtjtywtr73bbky4