@inproceedings{alvarez-gila_adversarial_2017,
title = {Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB},
author = {Aitor Alvarez-Gila and Joost van de Weijer and Estíbaliz Garrote},
url = {https://arxiv.org/abs/1709.00265},
doi = {10.1109/ICCVW.2017.64},
year = {2017},
date = {2017-01-01},
booktitle = {2017 IEEE International Conference on Computer Vision Workshops (ICCVW)},
pages = {480--490},
abstract = {Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer. Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However, most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can provide. We pose hyperspectral natural image reconstruction as an image to image mapping learning problem, and apply a conditional generative adversarial framework to help capture spatial semantics. This is the first time Convolutional Neural Networks -and, particularly, Generative Adversarial Networks- are used to solve this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop of 44.7% and a Relative RMSE drop of 47.0% on the ICVL natural hyperspectral image dataset.},
note = {The arxiv.org version contains updated results.},
keywords = {cnn, color, GAN, hyperspectral, neural networks},
pubstate = {published},
tppubtype = {inproceedings}
}
Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer. Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However, most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can provide. We pose hyperspectral natural image reconstruction as an image to image mapping learning problem, and apply a conditional generative adversarial framework to help capture spatial semantics. This is the first time Convolutional Neural Networks -and, particularly, Generative Adversarial Networks- are used to solve this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop of 44.7% and a Relative RMSE drop of 47.0% on the ICVL natural hyperspectral image dataset.
@article{rojo2017ladle,
title = {Ladle furnace slag characterization through hyperspectral reflectance regression model for secondary metallurgy process optimization},
author = {Asier Vicente and Artzai Picon and Sergio Rodríguez-Vaamonde and Jose Antonio Arteche and Jorge Armentia and Inaki Macaya},
url = {https://www.researchgate.net/publication/320395826_Fast_method_for_slag_characterization_during_ladle_furnace_steelmaking_process_based_on_spectral_reflectance},
year = {2017},
date = {2017-01-01},
journal = {IEEE Transactions on Industrial Informatics},
publisher = {IEEE},
keywords = {hyperspectral},
pubstate = {published},
tppubtype = {article}
}
@article{picon2012real,
title = {Real-time hyperspectral processing for automatic nonferrous material sorting},
author = {Artzai Picón and Aranzazu Bereciartua and Jone Echazarra and Ovidiu Ghita and Paul F Whelan and Pedro M Iriondo},
url = {https://computervision.tecnalia.com/wp-content/uploads/2012/12/2012_JEI_RealTimeHyperspectral.pdf},
year = {2012},
date = {2012-01-01},
journal = {Journal of Electronic Imaging},
volume = {21},
number = {1},
pages = {013018},
publisher = {International Society for Optics and Photonics},
keywords = {hyperspectral},
pubstate = {published},
tppubtype = {article}
}
@article{picon2011biologically,
title = {Bio-inspired Data Decorrelation Methodology for Hyperspectral Imaging},
author = {Artzai Picon and Ovidiu Ghita and Sergio Rodriguez-Vaamonde and Pedro Ma Iriondo and Paul F Whelan},
url = {https://computervision.tecnalia.com/wp-content/uploads/2012/12/2011_BiologicallyInspiredData.pdf},
year = {2011},
date = {2011-01-01},
journal = {EURASIP Journal on Advances in Signal Processing},
volume = {2011},
number = {1},
pages = {66},
publisher = {Springer},
keywords = {hyperspectral},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{picon2010automation,
title = {Automation of waste recycling using hyperspectral image analysis},
author = {Artzai Picón and Ovidiu Ghita and Pedro M Iriondo and Aranzazu Bereciartua and Paul F Whelan},
url = {https://computervision.tecnalia.com/wp-content/uploads/2012/12/2010_AutomationofWasteRecycling.pdf},
year = {2010},
date = {2010-01-01},
booktitle = {Emerging Technologies and Factory Automation (ETFA), 2010 IEEE Conference on},
pages = {1--4},
organization = {IEEE},
keywords = {hyperspectral, image processing},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{picon2010reciclaje,
title = {Reciclaje de chatarra electrónica: Nuevo algoritmo para su clasificación por imágenes hiperespectrales.},
author = {Artzai Picón-Ruiz and Jone Echazarra-Huguet and Arantza Bereciartua-Pérez},
url = {https://www.revistadyna.com/busqueda/reciclaje-de-chatarra-electronica-nuevo-algoritmo-para-su-clasificacion-por-imagenes-hiperespectrale},
year = {2010},
date = {2010-01-01},
journal = {DYNA-Ingeniería e Industria},
volume = {85},
number = {2},
keywords = {hyperspectral, industry},
pubstate = {published},
tppubtype = {article}
}
@article{gutierrez2010application,
title = {The application of hyperspectral image processing to the steel foundry process},
author = {JA Gutiérrez and A Picon and S Rodriguez and I Girbau},
url = {https://computervision.tecnalia.com/wp-content/uploads/2012/12/2010_ISIS_slag.pdf},
year = {2010},
date = {2010-01-01},
journal = {Proceedings of the International Surface Inspection Summit},
keywords = {biometrics, hyperspectral, industry},
pubstate = {published},
tppubtype = {article}
}
@article{rodriguez2010towards,
title = {Towards the ecological dredger},
author = {Sergio Rodríguez and Alberto Lago and Aritz Villodas and Artzai Picón},
url = {https://upcommons.upc.edu/handle/2099/8609},
year = {2010},
date = {2010-01-01},
journal = {Instrumentation viewpoint},
number = {8},
pages = {46--46},
publisher = {SARTI (Technological Development Centre of Remote Acquisition and Data processing Systems)},
keywords = {hyperspectral},
pubstate = {published},
tppubtype = {article}
}
@article{picon2009fuzzy,
title = {Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data},
author = {Artzai Picón and Ovidiu Ghita and Paul F Whelan and Pedro M Iriondo},
url = {https://computervision.tecnalia.com/wp-content/uploads/2012/12/2009_Fuzzy-Spectral-and-Spatial-Feature-Integration-for-Classification-of-Non-ferrous-Materials-in-Hyper-spectral-D.pdf},
year = {2009},
date = {2009-01-01},
journal = {IEEE Transactions on Industrial Informatics},
volume = {5},
number = {4},
pages = {483--494},
publisher = {IEEE},
keywords = {hyperspectral, industry},
pubstate = {published},
tppubtype = {article}
}
@phdthesis{ruiz2008classification,
title = {Classification of Materials Through the Integration of Spectral and Spatial Features from Hyperspectral Data: Doctoral Thesis},
author = {Artzai Picón Ruiz},
year = {2008},
date = {2008-01-01},
keywords = {hyperspectral},
pubstate = {published},
tppubtype = {phdthesis}
}
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