Morphological Texture Description For Hyperspectral Images: Pattern Spectrum

Deborah, Hilda; Richard, Noël
Peer reviewed, Journal article
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URI
https://hdl.handle.net/11250/2826190
Date
2021
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Original version
10.1109/WHISPERS52202.2021.9484052
Abstract
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.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Journal
Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Copyright
© IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.