High order singular value decomposition for plant diversity estimation

Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F21%3A87003" target="_blank" >RIV/60460709:41330/21:87003 - isvavai.cz</a>
Výsledek na webu
<a href="https://arxiv.org/abs/1911.13076" target="_blank" >https://arxiv.org/abs/1911.13076</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s40574-021-00300-w" target="_blank" >10.1007/s40574-021-00300-w</a>

Jazyk výsledku
angličtina
Název v původním jazyce
High order singular value decomposition for plant diversity estimation
Popis výsledku v původním jazyce
We propose a new method to estimate plant diversity with Renyi and Rao indexes through the so called High Order Singular Value Decomposition (HOSVD) of tensors. Starting from NASA multi-spectral images we evaluate diversity and we compare original diversity estimates with those realized via the HOSVD compression methods for big data. Our strategy turns out to be extremely powerful in terms of memory storage and precision of the outcome. The obtained results are so promising that we can support the efficiency of our method in the ecological framework.
Název v anglickém jazyce
High order singular value decomposition for plant diversity estimation
Popis výsledku anglicky
We propose a new method to estimate plant diversity with Renyi and Rao indexes through the so called High Order Singular Value Decomposition (HOSVD) of tensors. Starting from NASA multi-spectral images we evaluate diversity and we compare original diversity estimates with those realized via the HOSVD compression methods for big data. Our strategy turns out to be extremely powerful in terms of memory storage and precision of the outcome. The obtained results are so promising that we can support the efficiency of our method in the ecological framework.

Rok uplatnění
2021
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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