Title
A Metrological Framework For Hyperspectral Texture Analysis Using Relative Spectral Difference Occurrence Matrix
Abstract
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).
Year
DOI
Venue
2019
10.1109/WHISPERS.2019.8921335
2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Keywords
Field
DocType
Texture,non-uniformity,hyperspectral,metrology,Kullback-Leibler divergence
Texture Descriptor,Pattern recognition,Matrix (mathematics),Local binary patterns,Metrology,Hyperspectral imaging,Spectral line,Probability distribution,Artificial intelligence,Spectral bands,Mathematics
Conference
ISSN
ISBN
Citations 
2158-6268
978-1-7281-5295-0
0
PageRank 
References 
Authors
0.34
9
5
Name
Order
Citations
PageRank
Rui Jian Chu100.68
Noël Richard27416.52
Faouzi Ghorbel336146.48
Christine Fernandez-Maloigne417035.22
Jon Yngve Hardeberg536559.20