Morphological Texture Description For Hyperspectral Images: Pattern Spectrum

A metrological extension of morphological granulometry for the hyperspectral domain is introduced in this work. This development is enabled by the latest study of a suitable ordering relation for hyperspectral images. With granulometry as a texture descriptor, a suitable similarity measure for it is also introduced. In addition to providing validation experiments to the extension, a preliminary result in a texture discrimination task can also be found in this work.

• Publication year

  2021

• Venue

  Workshop on Hyperspectral Image and Signal Processing

• Publication date

  2021-03-24

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1109/WHISPERS52202.2021.9484052
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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