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Feature Recognition and Classification Based on Hyperspectral Data Mining

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DOI: 10.23977/geors.2022.050102 | Downloads: 24 | Views: 1035

Author(s)

Mingliang Wang 1, Ying LI 1, Canghai Guan 1, Hongli Li 1

Affiliation(s)

1 The Second Geodetic Surveying Brigade, Ministry of Natural Resources, Harbin, Heilongjiang 150025, China

Corresponding Author

Mingliang Wang

ABSTRACT

Since entering the 21st century, with the progress of various imaging spectrometer technologies and the rapid expansion of their application fields, hyperspectral remote sensing technology has been widely used in the fields of land and resources utilization, environmental monitoring, geological exploration, agricultural production, disaster early warning, urban planning, artificial target recognition and military detection. While providing more information than other remote sensing images, hyperspectral images have a large amount of data due to their many bands, which makes various ground object recognition and classification algorithms widely used in multispectral image processing slow and inefficient in hyperspectral image processing. Feature classification is an important field in digital image processing. A series of valuable data are obtained through the operation, extraction and classification of hyperspectral data images, and then applied in various fields. This paper studies the feature recognition and classification based on hyperspectral data mining. Hyperspectral data can accurately identify the common types of urban features, and the recognition method is particularly important for the results, and the fusion processing of hyperspectral images can improve the precision and accuracy of classification results to a certain extent.

KEYWORDS

Hyperspectral, Data mining, Feature recognition and classification

CITE THIS PAPER

Mingliang Wang, Ying LI, Canghai Guan, Hongli Li, Feature Recognition and Classification Based on Hyperspectral Data Mining. Geoscience and Remote Sensing (2022) Vol. 5: 11-15. DOI: http://dx.doi.org/10.23977/geors.2022.050102.

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