Machine Learning Using Hyperspectral Data Inaccurately Predicts Plant Traits Under Spatial Dependency
نویسندگان
چکیده
منابع مشابه
Processing Hyperspectral Data in Machine Learning
The adaptive and automated analysis of hyperspectral data is mandatory in many areas of research such as physics, astronomy and geophysics, chemistry, bioinformatics, medicine, biochemistry, engineering, and others. Hyperspectra di er from other spectral data that a large frequency range is uniformly sampled. The resulting discretized spectra have a huge number of spectral bands and can be seen...
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1 Helmholtz Centre for Environmental Research-UFZ, Computational Landscape Ecology, Permoserstraße 15, 04318 Leipzig, Germany; E-Mail: [email protected] 2 German Research Center for Geosciences, Section Remote Sensing, 14473 Potsdam, Germany; E-Mail: [email protected] 3 Department of Applied Environmental Science (ITM) and the Bert Bolin Centre for Climate Research, Stockholm Un...
متن کاملhigh performance of the support vector machine in classifying hyperspectral data using a limited dataset
to prospect mineral deposits at regional scale, recognition and classification of hydrothermal alteration zones using remote sensing data is a popular strategy. due to the large number of spectral bands, classification of the hyperspectral data may be negatively affected by the hughes phenomenon. a practical way to handle the hughes problem is preparing a lot of training samples until the size ...
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2018
ISSN: 2072-4292
DOI: 10.3390/rs10081263