A Metrological Framework for Hyperspectral Texture Analysis Using Relative Spectral Difference Occurrence Matrix
Peer reviewed, Journal article
Accepted version
Åpne
Permanent lenke
https://hdl.handle.net/11250/2655333Utgivelsesdato
2019Metadata
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Originalversjon
Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing. 2019, 2019-September . 10.1109/WHISPERS.2019.8921335Sammendrag
A new hyperspectral texture descriptor, Relative Spectral Difference Occurrence Matrix (RSDOM) is proposed. Developed in a metrological framework, it simultaneously considers the distribution of spectra and their spatial arrangement in the hyperspectral image. It is generic and adapted for any number of spectral band or range. As validation, a texture classification scheme is applied on HyTexiLa dataset using RSDOM. The obtained accuracy is excellent (95.6%), comparable to Opponent Band Local Binary Pattern (OBLBP) but at a much-reduced feature size (0.1% of OBLBP's).