High Order Singular Value Decomposition for Plant Biodiversity Estimation

TL;DR

This paper introduces a novel tensor-based method using High Order Singular Value Decomposition (HOSVD) to estimate plant biodiversity from multispectral images, demonstrating improved efficiency and accuracy over traditional methods.

Contribution

The paper presents a new HOSVD-based approach for biodiversity estimation that enhances storage efficiency and precision compared to existing techniques.

Findings

HOSVD method reduces storage requirements for biodiversity data.

The approach yields more precise biodiversity estimates from multispectral images.

Results support the method's effectiveness in ecological applications.

Abstract

We propose a new method to estimate plant biodiversity with R{\'e}nyi and Rao indexes through the so called High Order Singular Value Decomposition (HOSVD) of tensors. Starting from NASA multispectral images we evaluate biodiversity and we compare original biodiversity estimates with those realised via the HOSVD compression methods for big data. Our strategy turns out to be extremely powerful in terms of storage memory and precision of the outcome. The obtained results are so promising that we can support the efficiency of our method in the ecological framework.