The fusion of multispectral (MS) and hyperspectral (HS) images has recently been put in the spotlight. The combination of high spatial resolution MS images with HS data showing a lower spatial resolution but a more accurate spectral resolution is the aim of these techniques. This survey presents a deep review of the literature designed for students and professionals who want to know more about the topic. The basis aspects of the MS and HS image fusion are presented and the related approaches are classified into three different classes (pansharpening-based, decomposition-based, and machine learning-based). The ending part of this survey is devoted to the description of widely used datasets for this task and the performance assessment problem, even describing open issues and drawing guidelines for future research.

Multispectral and hyperspectral image fusion in remote sensing: A survey

Gemine Vivone
2023

Abstract

The fusion of multispectral (MS) and hyperspectral (HS) images has recently been put in the spotlight. The combination of high spatial resolution MS images with HS data showing a lower spatial resolution but a more accurate spectral resolution is the aim of these techniques. This survey presents a deep review of the literature designed for students and professionals who want to know more about the topic. The basis aspects of the MS and HS image fusion are presented and the related approaches are classified into three different classes (pansharpening-based, decomposition-based, and machine learning-based). The ending part of this survey is devoted to the description of widely used datasets for this task and the performance assessment problem, even describing open issues and drawing guidelines for future research.
2023
Istituto di Metodologie per l'Analisi Ambientale - IMAA
Multispectral imaging
Hyperspectral imaging
Pansharpening
Machine learning
Sparse representation
Low-rank
Tensors
Super-resolution
Image fusion
Remote sensing
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/456667
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