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High within-class variance as well as low between-class variance that characterize this kind of imagery make the detection and classification of ground cover transitions a difficult task.
The ground conditions of the points with a predominant period in the range of approximately 0.4 0.8 s are considered to be soft ground, according to the classification of ground conditions (e.g. Molas and Yamazaki, 1995).
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Hence, there is a high demand for automatic classification of ground-based cloud.
Cloud classification of ground-based images is a challenging task due to extreme variations under different atmospheric conditions.
The issue of T/pT classification of ground-glass opacities (GGOs) deserves special attention as these are increasingly found at CT-scan screening, with or without a solid component.
The classification of the ground behaviors are carried out according to the established delimiting criteria.
The traditional classification of frozen ground based on the areal distribution of permafrost is too generalized for engineering purposes and a more refined classification is necessary for engineering design and construction.
Hydro-geochemical classification of the ground water revealed that most of the samples were CaHCO3 type or mixed CaNaHCO3 type.
Given the actual classification of pixels (ground truth) and the output of the system, four outcomes are possible.
Quantitative classification of near-fault ground motions using wavelet analysis.
Quantitative classification of near-fault ground motions using wavelet analysis, Bulletin of the Seismological Society of America, 97 (5), 1486-1501.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com