Multispectral and hyperspectral image fusion in remote sensing: A survey

被引:70
|
作者
Vivone, Gemine [1 ]
机构
[1] CNR, Inst Methodol Environm Anal, I-85050 Tito, Italy
关键词
Multispectral imaging; Hyperspectral imaging; Pansharpening; Machine learning; Sparse representation; Low-rank; Tensors; Super-resolution; Image fusion; Remote sensing; SOIL ORGANIC-CARBON; TENSOR FACTORIZATION; VEGETATION INDEXES; MULTISCALE FUSION; ENMAP DATA; SUPERRESOLUTION; RESOLUTION; HYPERION; NETWORK; QUALITY;
D O I
10.1016/j.inffus.2022.08.032
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:405 / 417
页数:13
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