Haut, Juan M.Moreno Álvarez, SergioMoreno Ávila, EnriqueAyma Quirita, Victor AndrésPastor Vargas, RafaelPaoletti, Mercedes Eugenia2024-11-192024-11-192023J. M. Haut, S. Moreno-Álvarez, E. Moreno-Ávila, V. A. Ayma, R. Pastor-Vargas and M. E. Paoletti, "Cloud Implementation of Extreme Learning Machine for Hyperspectral Image Classification," in IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023, Art no. 5506905, doi: 10.1109/LGRS.2023.32957421545-598X, eISSN: 1558-0571https://doi.org/10.1109/LGRS.2023.3295742https://hdl.handle.net/20.500.14468/24421The registered version of this article, first published in “IEEE Geoscience and Remote Sensing Letters, vol. 20, 2023", is available online at the publisher's website: IEEE, https://doi.org/10.1109/LGRS.2023.3295742 La versión registrada de este artículo, publicado por primera vez en “IEEE Geoscience and Remote Sensing Letters, vol. 20, 2023", está disponible en línea en el sitio web del editor: IEEE, https://doi.org/10.1109/LGRS.2023.3295742Classifying remotely sensed hyperspectral images (HSIs) became a computationally demanding task given the extensive information contained throughout the spectral dimension. Furthermore, burgeoning data volumes compound inherent computational and storage challenges for data processing and classification purposes. Given their distributed processing capabilities, cloud environments have emerged as feasible solutions to handle these hurdles. This encourages the development of innovative distributed classification algorithms that take full advantage of the processing capabilities of such environments. Recently, computational-efficient methods have been implemented to boost network convergence by reducing the required training calculations. This letter develops a novel cloud-based distributed implementation of the extreme learning machine ( CC-ELM ) algorithm for efficient HSI classification. The proposal implements a fault-tolerant and scalable computing design while avoiding traditional batch-based backpropagation. CC-ELM has been evaluated over state-of-the-art HSI classification benchmarks, yielding promising results and proving the feasibility of cloud environments for large remote sensing and HSI data volumes processing. The code available at https://github.com/mhaut/scalable-ELM-HSIeninfo:eu-repo/semantics/openAccess12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informáticaartículotraininghyperspectral imagingcloud computingclassification algorithmsscalabilityproposalscluster computing