Coniferous Trees Needles-Based Taxonomy Classification

0520496 - ÚTIA 2020 RIV US eng C - Conference Paper (international conference)
Haindl, Michal - Žid, Pavel
Coniferous Trees Needles-Based Taxonomy Classification.
International Conference on Image and Vision Computing New Zealand 2019 (IVCNZ 2019). Piscataway: IEEE, 2019, s. 1-6. ISBN 978-1-7281-4188-6. ISSN 2151-2191.
[Image and Vision Computing New Zealand (IVCNZ 2019) /34./. Dunedin (NZ), 02.12.2019-04.12.2019]
R&D Projects: GA ČR(CZ) GA19-12340S
Institutional support: RVO:67985556
Keywords : Coniferous needles categorization * Tree taxonomy recognition * Spiral Markov random field model
OECD category: Automation and control systems
http://library.utia.cas.cz/separaty/2020/RO/haindl-0520496.pdf

This paper introduces multispectral rotationally invariant textural features of the Markovian type applied for the effective coniferous tree needles categorization. Presented texture features are inferred from the descriptive multispectral spiral wide-sense Markov model. Unlike the alternative texture recognition methods based on various gray-scale discriminative textural descriptions, we take advantage of the needles texture representation, which is fully descriptive multispectral and rotationally invariant. The presented method achieves high accuracy for needles recognition. Thus it can be used for reliable coniferous tree taxon classification. Our classifier is tested on the open source needles database Aff, which contains 716 high-resolution images from 11 diverse coniferous tree species.
Permanent Link: http://hdl.handle.net/11104/0305414