Work in progress — this section is still being reviewed and updated.

Thesis on multispectral image fusion (thermal and RGB) and its methodological study through deep learning, extended to multisensor integration including LiDAR.

Publications and References (6)

  • Preprints Enrique Heredia-Aguado, Alejandro Rujano, Mónica Galdeano, David Valiente, Arturo Gil. (2026). The Impact of Performance Variability on Neural Network Evaluation and its Implications for Model Design. Preprint.
  • Conference Papers Enrique Heredia-Aguado, Maria Flores, Monica Ballesta, David Valiente, Luis Paya, Arturo Gil, Juan Jose Cabrera. (2026). Fusion temprana de imagenes multiespectrales mediante tecnicas de reproyeccion para tareas de deteccion mediante algoritmos profundos: Un estudio comparativo. Simposio CEA de Robotica, Bioingenieria, Vision Artificial y Automatica Marina. Vol. 2(2).
  • Journal Articles Enrique Heredia-Aguado, Juan Jose Cabrera, Luis Miguel Jimenez, David Valiente, Arturo Gil. (2025). Static Early Fusion Techniques for Visible and Thermal Images to Enhance Convolutional Neural Network Detection: A Performance Analysis. Remote Sensing. Vol. 17(6).
  • Conference Papers Enrique Heredia-Aguado, Marcos Alfaro-Perez, Maria Flores, Luis Paya, David Valiente, Arturo Gil. (2025). A Robust Comparative Study of Adaptative Reprojection Fusion Methods for Deep Learning Based Detection Tasks with RGB-Thermal Images. Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics (ICINCO).
  • Conference Papers Enrique Heredia-Aguado, David Valiente, Arturo Gil, Miriam Maximo, Luis Paya. (2024). Fusion estatica de imagenes del espectro visible y termico para una mejor deteccion de personas mediante Redes Neuronales Convolucionales: un analisis del rendimiento. Actas del Simposio de Robotica, Bioingenieria y Vision por Computador.
  • Presentations Enrique Heredia-Aguado, Alejandro Rujano, David Valiente, Arturo Gil. (2026). Tecnicas de fusion de imagenes multiespectrales para una mejor deteccion mediante algoritmos de aprendizaje profundo. VI Congreso Anual de Estudiantes de Doctorado (UMH).