Hauptseite > Publikationsdatenbank > Remote Sensing Image Classification Using CNNs With Balanced Gradient for Distributed Heterogeneous Computing > print

001     909066
005     20230123110638.0
024 7 _ |a 10.1109/LGRS.2022.3173052
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024 7 _ |a 1545-598X
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024 7 _ |a 1558-0571
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024 7 _ |a 2128/31655
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024 7 _ |a WOS:000800169600001
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037 _ _ |a FZJ-2022-02984
082 _ _ |a 550
100 1 _ |a Moreno-Alvarez, Sergio
|0 0000-0002-1858-9920
|b 0
245 _ _ |a Remote Sensing Image Classification Using CNNs With Balanced Gradient for Distributed Heterogeneous Computing
260 _ _ |a New York, NY
|c 2022
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520 _ _ |a Land-cover classification methods are based on the processing of large image volumes to accurately extract representative features. Particularly, convolutional models provide notable characterization properties for image classification tasks. Distributed learning mechanisms on high-performance computing platforms have been proposed to speed up the processing, while achieving an efficient feature extraction. High-performance computing platforms are commonly composed of a combination of central processing units (CPUs) and graphics processing units (GPUs) with different computational capabilities. As a result, current homogeneous workload distribution techniques for deep learning (DL) become obsolete due to their inefficient use of computational resources. To address this, new computational balancing proposals, such as heterogeneous data parallelism, have been implemented. Nevertheless, these techniques should be improved to handle the peculiarities of working with heterogeneous data workloads in the training of distributed DL models. The objective of handling heterogeneous workloads for current platforms motivates the development of this work. This letter proposes an innovative heterogeneous gradient calculation applied to land-cover classification tasks through convolutional models, considering the data amount assigned to each device in the platform while maintaining the acceleration. Extensive experimentation has been conducted on multiple datasets, considering different deep models on heterogeneous platforms to demonstrate the performance of the proposed methodology.
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700 1 _ |a Paoletti, Mercedes E.
|0 0000-0003-1030-3729
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700 1 _ |a Cavallaro, Gabriele
|0 P:(DE-Juel1)171343
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700 1 _ |a Rico, Juan A.
|0 0000-0002-4264-7473
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700 1 _ |a Haut, Juan M.
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773 _ _ |a 10.1109/LGRS.2022.3173052
|g Vol. 19, p. 1 - 5
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|t IEEE geoscience and remote sensing letters
|v 19
|y 2022
|x 1545-598X
856 4 _ |u https://juser.fz-juelich.de/record/909066/files/Moreno_GRSL_Preprint.pdf
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