FIELD: data processing.
SUBSTANCE: invention relates to methods of processing hyperspectral data and can be used for recognition of images of biochemical ground objects with fine spectral differences. Essence: digital hyperspectral ground and space probing data processed to a spectral brightness factor are recorded on a magnetic carrier. Representative training sample of the minimum required volume is obtained in conditions for estimating a lower bound on the probability of the classification error. Informative spectral features are extracted based on the found non-uniform training sample potentially containing information on change of percentage content of a chemical substance in vegetation. Volume of the training sample is specified as per the optimum ratio of the number of the found spectral features to the volume of the training sample. Classifier is constructed based on the refined training sample.
EFFECT: high accuracy of classifying images of biochemical ground objects with fine spectral differences.
1 cl, 4 tbl
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Authors
Dates
2019-07-29—Published
2018-11-28—Filed