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Fecha
2025-10-23
Derechos de acceso
info:eu-repo/semantics/embargoedAccess
Título de la revista
ISSN de la revista
Título del volumen
Editorial
Institute of Electrical and Electronics Engineers
Citas
Resumen
Hyperspectral unmixing addresses the challenge of mixed pixels in hyperspectral images by identifying the number of pure pixels (endmembers), extracting their spectral signatures, and estimating their proportions (abundances) in each pixel composing the scene. Traditional hyperspectral unmixing methods often struggle with scalability and computational efficiency when dealing with gigabyte-scale datasets. In this article, we propose a distributed parallel geometric distance (DPGD) method for hyperspectral unmixing, exploring the computational power and benefits of distributed parallel processing within a distributed computing framework. The proposed DPGD leverages geometric distance measurements to accurately identify endmembers and estimate their abundances, taking into account the intrinsic similarities within hyperspectral images. This provides a clearer representation of the data structure, leading to improved unmixing accuracy. By using the Spark programming model, the computational workload is efficiently distributed across multiple nodes, significantly reducing processing time. Experimental results on real hyperspectral datasets demonstrate that DPGD scales effectively up to 32 nodes and 290.9 GB of data, achieving competitive accuracy and efficiency compared to state-of-the-art methods. The code is available at https://github.com/ccaadaro/DPDG
Descripción
The registered version of this article, first published in IEEE Transactions on Geoscience and Remote Sensing, is available online at the publisher's website: Institute of Electrical and Electronics Engineers, https://doi.org/10.1109/TGRS.2025.3624287
La versión registrada de este artículo, publicado por primera vez en IEEE Transactions on Geoscience and Remote Sensing, está disponible en línea en el sitio web del editor: Institute of Electrical and Electronics Engineers, https://doi.org/10.1109/TGRS.2025.3624287
La versión registrada de este artículo, publicado por primera vez en IEEE Transactions on Geoscience and Remote Sensing, está disponible en línea en el sitio web del editor: Institute of Electrical and Electronics Engineers, https://doi.org/10.1109/TGRS.2025.3624287
Categorías UNESCO
Palabras clave
Hyperspectral unmixing, distributed parallel geometric distance, Big data
Citación
Canada, C., Paoletti, M. E., Garcia-Flores, M. B., Tao, X., Pastor-Vargas, R., & Haut, J. M. (2025). Distributed Parallel Hyperspectral Unmixing for Large-Scale Data in Spark Environments via Geometric Distance. IEEE Transactions on Geoscience and Remote Sensing, 63. https://doi.org/10.1109/TGRS.2025.3624287
Centro
E.T.S. de Ingeniería Informática
Departamento
Sistemas de Comunicación y Control