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245 _ _ |a Proven Approaches of Using Innovative High-Performance Computing Architectures in Remote Sensing
260 _ _ |a Boca Raton
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295 1 0 |a Signal and Image Processing for Remote Sensing
300 _ _ |a 432
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520 _ _ |a This chapter underscores the essential role of high-performance computing (HPC) in the realm of remote sensing (RS), effectively addressing the growing demand for processing extensive and complex datasets. HPC, empowered by parallel programming paradigms, significantly speeds up a range of tasks, including image processing, data mining, and modeling, vital in the context of Earth observation (EO) applications. More notably, HPC can build even better models by employing systematic hyperparameter optimization methods that are computationally demanding, given a large search space. Furthermore, with deep learning (DL) progressively gravitating toward foundation models, extensively trained on substantial datasets, endowing them with the remarkable capability to transfer knowledge across diverse tasks, there is an increased demand for computational resources in the fast-paced landscape of artificial intelligence (AI) and consequently a heightened interest in HPC. Solutions to provide optimized resources on HPC resources, however, have increased their complexity and heterogeneity. This chapter highlights the advantages of embracing HPC while acknowledging current challenges, solutions, and future trends.
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700 1 _ |a Cavallaro, Gabriele
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700 1 _ |a Riedel, Morris
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700 1 _ |a Benediktsson, Jón Atli
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856 4 _ |u https://doi.org/10.1201/9781003382010
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