• Reliable Explainability of Deep Learning Spatial-Spectral Classifiers for Improved Semantic Segmentation in Autonomous Driving 

  Gutiérrez Zaballa, Jon; Basterrechea Oyarzabal, Koldobika; Echanove Arias, Francisco Javier (IEEE, 2025-02-19)

  Integrating hyperspectral imagery (HSI) with deep neural networks (DNNs) can strengthen the accuracy of intelligent vision systems by combining spectral and spatial information, which is useful for tasks like semantic ...
• Designing DNNs for a trade-off between robustness and processing performance in embedded devices 

  Gutiérrez Zaballa, Jon; Basterrechea Oyarzabal, Koldobika; Echanove Arias, Francisco Javier (IEEE, 2024-12-03)

  Machine learning-based embedded systems employed in safety-critical applications such as aerospace and autonomous driving need to be robust against perturbations produced by soft errors. Soft errors are an increasing concern ...
• Rapid Deployment of Domain-specific Hyperspectral Image Processors with Application to Autonomous Driving 

  Gutiérrez Zaballa, Jon; Basterrechea Oyarzabal, Koldobika; Echanove Arias, Francisco Javier ; Mata Carballeira, Oscar ; Martínez González, María Victoria (IEEE, 2024-01-10)

  The article discusses the use of low cost System-On-Module (SOM) platforms for the implementation of efficient hyperspectral imaging (HSI) processors for application in autonomous driving. The work addresses the challenges ...
• HSI-Drive v2. 0: More Data for New Challenges in Scene Understanding for Autonomous Driving 

  Gutiérrez Zaballa, Jon; Basterrechea Oyarzabal, Koldobika; Echanove Arias, Francisco Javier ; Martínez González, María Victoria; Martínez Corral, Unai (IEEE, 2024-01-01)

  We present the updated version of the HSI-Drive dataset aimed at developing automated driving systems (ADS) using hyperspectral imaging (HSI). The v2.0 version includes new annotated images from videos recorded during ...

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