Hypercomplex Quality Assessment of Multi/Hyperspectral Images

By Andrea Garzelli, Filippo Nencini. IEEE Geoscience and Remote Sensing Letters, 2009

375 citations recorded by the source corpus.

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Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, methodology): “[79] A.”
Pixel-level image fusion: A survey of the state of the art (2017, background): “During the last decade, a number of new 345 objective metrics have been proposed as better alternatives [92–97].”
A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods (2021, methodology): “Q2n Q2 is the multiband extension of the universal image quality index (UIQI) [140] and was introduced for quality assessment of pansharpened MS images, first for four bands [141] and later extended to 2 bands [142].”
Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature (2017, methodology): “Q2 n can be computed using the hypercomplex correlation coefficient, which jointly quantifies spectral and spatial distortions [80].”
A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening (2017, background): “metric (Q) [45], the Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS) [46], the spectral angle mapper (SAM) [47], and Q2n: An expanded version of Q that adds spectral fidelity into consideration [48].”