We propose a new method to estimate plant diversity with Rényi 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.
High order singular value decomposition for plant diversity estimation / Bernardi, Alessandra; Iannacito, Martina; Rocchini, Duccio. - In: BOLLETTINO DELLA UNIONE MATEMATICA ITALIANA. - ISSN 1972-6724. - 2021/14:4(2021), pp. 557-591. [10.1007/s40574-021-00300-w]
High order singular value decomposition for plant diversity estimation
Bernardi, Alessandra;
2021-01-01
Abstract
We propose a new method to estimate plant diversity with Rényi 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.| File | Dimensione | Formato | |
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